Anemia, a condition characterized by the lack of healthy red blood cells in the body, is common in patients with chronic kidney disease who need to undergo routine hemodialysis (a process that helps to "clean" the blood when the kidneys don't function well). Thus, red blood cell-stimulating agents (called "erythropoiesis-stimulating agents" or ESAs) and iron supplements (ISs) are administered as part of this process. But, complications can arise if the patients have an altered iron metabolism or poor response to medications. Moreover, the medications tend to be expensive and impose a heavy financial burden on public health. Thus, with such patients currently on the rise but not enough physicians suitably trained to administer treatment, additional support systems with smart decision-making capabilities are highly sought after. One option is to turn to artificial intelligence (AI), which seems promising but demands a large dataset and is not practical owing to diverse health conditions of patients.

So, can something be done to improve the situation? In a recent study published in the International Journal of Medical Sciences, medical researchers from Japan tried to find the answer. They came up with a new approach: instead of making the AI learn from the complex physiology of the patient's body, they opt for a prediction model based on the decisions of experienced physicians. Assistant Professor Toshiaki Ohara from Okayama University, Japan, the lead scientist on the study, explains, "We got the idea while contemplating the thought process of seasoned physicians. After all, they do not calculate detailed values of vital reactions in a patient's body when deciding dosages, which means prediction models based on biochemistry are not necessary."

The researchers started off by collecting clinical data at two hospitals in Japan and then preparing two datasets for each hospital: one for training their model and the other for testing and validating its predictions. Simultaneously, they recorded the dosage directions of physicians at both hospitals and considered responses for the two medications used during hemodialysis: ESAs and ISs.

Based on these, they constructed an AI-based model called an "artificial-intelligence-supported anemia control system" (AISACS), which received a total of five inputs (four items of blood examination and dosage history) and churned out dosage direction probabilities for the two medications as outputs. In addition, to make the training process more efficient, they compensated for the time lag between blood examination and dosage decisions by using "data rectification" to match the decision dates with the examination dates.

To the researchers' delight, AISACS showed a high prediction accuracy with correct classification (directions matching those of physicians) rates of 72%-87%. But what was even more interesting was that it provided "clinically appropriate" classifications at even higher rates (92%-97%). These were directions that didn't match those of physicians (and were sometimes provided ahead of them) but were still considered appropriate from a medical viewpoint.

With these results, researchers are hopeful about AISAC's future prospects. "By preventing anemia, our system can help alleviate the burdens on physicians and medical insurance systems. Moreover, it has the potential to share the knowledge and experiences related to medications," comments an excited Dr. Ohara.

Hopefully, this new AI-based approach provides some hope to both patients undergoing hemodialysis and physicians treating them.

Source: EurekAlert!

While 2020 incited widespread adoption of video-based consultations, the healthcare ecosystem is now presented with a new mandate of delivering virtual care at scale. Fortunately, there are some companies up to the task.

After crossing the one-year mark since the onset of the pandemic there is no question that our healthcare system has undergone transformational change. For the first time in history, consumers stopped visiting their medical provider for fear of their personal health. In return, virtual care was thrusted to the center stage of healthcare delivery as a panacea to enable safe, socially distant care. Telehealth adoption spiked at unprecedented rates, with some providers reporting 175 times the number of consultations via telehealth compared to pre-pandemic visits.

However, the healthcare system was woefully unprepared to deliver virtual care at scale, forcing clinicians to scramble and adopt solutions purpose-built for other use cases (such as Zoom and Facetime) as provider organizations lacked the technology infrastructure to deliver efficient and HIPAA compliant virtual care. In time, healthcare systems dedicated substantial resources to respond to the pandemic including meaningful investments in video-based telehealth platforms. Despite being fraught with challenges, telehealth adoption remains well above pre-pandemic utilization levels.

Now, as society begins the shift to the new normal with an increasing administration of vaccines, it is expected that consumers, providers, and payers alike will remain engaged with virtual care solutions due to the core benefit of convenience. Consumers have appreciated the expanded access to medical care and on-demand flexibility of telehealth, with over half reporting a willingness to use telehealth services post-pandemic. Providers too have enjoyed the greater flexibility and improved care coordination with over 95% reporting a willingness to use telehealth. With this momentum, payers are continuing to incentivize virtual care in order to provide members with higher quality service at a lower cost.

The past year successfully ushered in “Virtual Care 1.0”, the first wave of care innovation that focused on delivering traditional in-person care visits virtually. While convenient, Virtual Care 1.0 inhibits scalability for providers and limits flexibility for the consumer. Though substantial resources have been invested to better leverage telehealth, there is still a long way to go– today only 43% of consumers have adopted video-based telemedicine in 2020. Platforms that require the use of broadband internet or smartphones still exclude a sizeable portion of the population from accessing care. Seniors, as one example, have received inadequate medical care during the pandemic as more than 41% of Medicare members lack access to a computer with high-speed internet. Further, it is estimated that the US will face a shortage of up to 139K physicians by 2033, prompting a need to deliver care that moves beyond the one-to-one provider-to-patient model. While telemedicine has certainly brought convenience, provider scalability and consumer access still remain limited.

Now with a captive audience, it is time to look to the future and harness the power of virtual care to move beyond what was initial progress with Virtual Care 1.0 – solutions offering convenience, to what I will designate as Virtual Care 2.0 – solutions providing true systemic scalability.

Moving to Virtual Care 2.0

While 2020 incited widespread adoption of video-based consultations, the healthcare ecosystem is now presented with a new mandate of delivering virtual care at scale. Fortunately, a host of digital health solutions have emerged with the proposition of expanding platform functionality, broadening use cases, and enabling care delivery across new sites of care.

Expanded Platform Functionality

New digital health solutions are expanding the core functionality of telehealth platforms to expand beyond the confines of one-to-one video-chatting and improve provider efficiency. Underlying technology advancements are already being adopted to enable provider scalability. Companies like PathAI and Olive are leveraging artificial intelligence and natural language processing tools to provide triage, diagnostic support, and workflow process improvements so providers can focus their time on consumer care rather than administrative paperwork.

Several solutions have also emerged with the aim of expanding telehealth’s video-chatting functionality to enable omni-channel communication across asynchronous text, AI-enabled chat, and audio. Such platform enhancements will also satiate consumer’s interest in multi-modal communication and also enable physicians to expand from a one-to-one to a one-to-many model without increases in time or cost. CirrusMD and 98point6 is an examples of a company that are building text-first virtual care delivery platforms, allowing consumers to directly communicate with providers through asynchronous messaging. As 66% of consumers were equally or more satisfied with their text-based telemedicine communication vs. in-person interactions, such solutions allow consumers to receive care on their time. Text-based models also break down access barriers, particularly for vulnerable Medicaid populations that may lack video-chatting capabilities.

Broadening Clinical Use Cases

Second, the use cases of telemedicine are expanding beyond common primary care conditions. Advances in remote monitoring and at-home diagnostic testing are allowing providers to deliver virtual care to patients with more complex and chronic conditions. Companies like UDoTest offer at-home diagnostics for 100+ diseases, such as colon screening and pap smears, that now enables telehealth platforms to scale and provide care for millions of Americans suffering from specialty conditions.

Moreover, emerging digital health companies are building disease-specific telehealth platforms to address higher acuity conditions. For example, companies like NOCD and Equip Health are demonstrating strong outcomes in tackling severe mental health illnesses as consumers can better identify triggers in their own environment rather than from the four walls of a provider’s office. Other companies like Oshi Health and Vivante Health are utilizing telemedicine for gastrointestinal specialty care to connect consumers with multidisciplinary team involving GI specialists, dieticians, and health coaches. By advancing diagnostic and monitoring technology, telemedicine can rapidly scale and become part of a consumer’s entire health journey from primary care needs to specialty care.

Expanding Sites of Care

Throughout Covid-19, virtual care was primarily accessed by consumers from their homes for urgent care needs due to social distancing mandates. Emerging from the pandemic, digital health solutions can break down walls to deliver preventive and proactive care by meeting consumers where they are – at grocery stores, gyms, community centers, faith-based institutions, and homeless shelters. By integrating medical care into communities, virtual care can also help bridge the digital divide and overcome access barriers for hard-to-reach populations. Companies like Higi and Advinow Medical are leveraging AI technology and augmented reality so patients can self-measure vitals at a medical kiosk located at retail stores, pharmacies, or urgent care clinics. Such models will be critical for closing health equity gaps for hard-to-reach populations by providing consumers with on-demand access to clinical-grade diagnostics and evidence-based health screening tools at the places they already visit.

Other companies such as OnMed are bringing comprehensive primary care to airports, colleges, and hotels through telemedicine exam stations. The company’s stations are equipped with thermal imaging to read body temperature and diagnose infections, while also leveraging high-definition video and audio for virtual visits. The kiosk is equipped to dispense prescribed medications to truly provide consumers with a one-stop comprehensive experience.

There is no doubt that 2020 was transformational for virtual care as expanded convenience and access were critical to address the challenges presented by Covid-19. While traditional telehealth solutions allowed society to shift basic healthcare online, such models will need to expand to meet the growing demand from consumers for flexible, on-demand, and affordable care. As we contain Covid-19, 2021 will be seen as the year that saw the advent of Virtual Care 2.0. The newer virtual care solutions with their expanded platform functionality will be able to address a broader range of use cases, giving healthcare stakeholders the opportunity to rethink where care can and should be delivered.

Source: MedCityNews

AppliedVR, a pioneer advancing the next generation of digital medicine, today announced results from its pivotal randomized controlled trial (RCT), evaluating virtual reality (VR) therapy for treating chronic pain at home. The study, which was published in Journal of Medical Internet Research (JMIR), found that AppliedVR’s EaseVRx device produced “clinically meaningful” improvement in multiple pain outcomes, and had high participant satisfaction and engagement.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20210224005301/en/

This study comes on the heels of EaseVRx receiving FDA Breakthrough Device Designation last October. EaseVRx is an eight-week program delivered via a virtual reality medical device which teaches participants how to recognize and adjust cognitive, emotional, and physical responses to chronic pain using one session per day.

Conducted remotely due to COVID-19, the double-blinded national study analyzed data from 179 individuals in the U.S. who reported experiencing chronic low-back pain for at least six months. On average, participants in the EaseVRx group reported substantial improvements at post-treatment, including:

•42% reduction in pain intensity;

•49% reduction in activity interference;

•52% reduction in sleep interference;

•56% reduction in mood interference; and

•57% reduction in stress interference.

Overall, EaseVRx had a high treatment-response rate compared to the control, including:

•87% of participants experienced reduction in pain;

•65% of participants experienced at least a 30% reduction in pain; and

•46% of participants experienced at least a 50% reduction in pain - average pain reduction in this group of 71%.

Researchers also reported that 91% of participants completed the full eight-week program. Of note, system usability for EaseVRx was in the 96-100th percentile based on the System Usability Scale rating, representing an A+ usability grade, a score that beats some of the prolific digital products used by consumers (e.g. an ATM, top email provider and a major e-commerce platform). Engagement and usability data are critical to providers and payers who must evaluate the likelihood that members/patients will use a digital therapeutic -- especially on themselves outside of clinical settings.

“Most often pain is treated with a purely biomedical approach, using medications or procedures. Currently, lower-risk treatment options are not available at scale," said Dr. Beth Darnall1, AppliedVR chief science advisor, who co-authored the study. "Our findings show that VR for chronic pain can provide effective on-demand, home-based pain care at scale. Home-based VR may improve the risk-benefit profile well above the current standard of care."

The COVID-19 pandemic has created an urgent need for effective digital therapeutics to address the nation’s largest health problems. To meet the surge in demand, providers, insurers and policymakers have taken measures to expand access to digital therapeutics. In January, the Centers for Medicare and Medicaid Services (CMS) finalized a rule that creates a new, accelerated Medicare coverage pathway for innovative products that the FDA deems “breakthrough.” Under the rule, Medicare can provide national coverage simultaneously with FDA approval for a four-year evaluation period, allowing device developers to build a greater body of evidence for their solutions.

This development, along with similar moves by some commercial insurers, comes as welcomed news to people suffering from chronic pain, many of whom are seniors, who traditionally have relied on pharmaceuticals. Chronic pain is an extremely costly and complex problem in the U.S, with The Institute of Medicine estimating2 that one in three (approximately 100 million) Americans are living with some type of ongoing pain. A previous Johns Hopkins study published in The Journal of Pain3 found that the annual cost of chronic pain could be as high as $635 billion a year, which is more than the yearly costs for cancer, heart disease and diabetes combined.

“After yet another successful clinical trial, we’ve reinforced AppliedVR’s unwavering commitment to being the most effective, cost-conscious and data-backed VR-based solution for chronic pain on the market,” said Josh Sackman, co-founder and president of AppliedVR. “And now that CMS and forward-thinking commercial payers are seeing the outcomes that digital therapeutics like ours can deliver, we fully expect EaseVRx to soon be a provider-prescribed, payer-reimbursed treatment for multiple chronic-pain indications.”

Already the most evidence-backed VR provider in healthcare, AppliedVR is engaged with multiple big-name insurers to evaluate its platform as a covered treatment for chronic pain.

Last June, AppliedVR published the first scientific study using VR to treat chronic pain at home, and has previously partnered with University of California at San Francisco (UCSF) to study how digital therapeutic platforms, including virtual and augmented reality, can be used to improve care access for underserved populations. Additionally, partly funded by $2.9 million in grants from the National Institute on Drug Abuse (NIDA), the company also is advancing two clinical trials with Geisinger and Cleveland Clinic to study VR as an opioid-sparing tool for acute and chronic pain.

About AppliedVR

AppliedVR is a leader in digital therapeutics, pioneering virtual reality-based treatments that address the complexity of chronic pain. Our mission is to empower patients with the tools to live life, beyond chronic pain. Rooted in cognitive behavioral therapy and mindfulness, AppliedVR’s EaseVRx is the first VR-based prescription therapeutic to receive ‘Breakthrough Therapy Designation’ by the FDA. Offering a comprehensive approach that encompasses the biological, psychological and social factors that influence how people experience chronic pain, EaseVRx enables patients to change the way they process pain and develop new, positive habits and coping skills that improve quality of life. Patients can easily self-administer EaseVRx in the comfort of their own homes, at any time, without restrictions tied to a healthcare professional’s schedule – advancing remote care as well as quality, equity and efficiency in chronic pain management.

Source: AppliedVR

University of California San Diego device monitors cardiovascular signals and multiple biochemical levels.

Engineers at the University of California San Diego have developed a soft, stretchy skin patch that can be worn on the neck to continuously track blood pressure and heart rate while measuring the wearer’s levels of glucose as well as lactate, alcohol or caffeine. It is the first wearable device that monitors cardiovascular signals and multiple biochemical levels in the human body at the same time.

“This type of wearable would be very helpful for people with underlying medical conditions to monitor their own health on a regular basis,” said Lu Yin, a nanoengineering Ph.D. student at UC San Diego and co-first author of the study. “It would also serve as a great tool for remote patient monitoring, especially during the COVID-19 pandemic when people are minimizing in-person visits to the clinic.”

Such a device could benefit individuals managing high blood pressure and diabetes—individuals who are also at high risk of becoming seriously ill with COVID-19. It could also be used to detect the onset of sepsis, which is characterized by a sudden drop in blood pressure accompanied by a rapid rise in lactate level.

One soft skin patch that can do it all would also offer a convenient alternative for patients in intensive care units, including infants in the NICU, who need continuous monitoring of blood pressure and other vital signs. These procedures currently involve inserting catheters deep inside patients’ arteries and tethering patients to multiple hospital monitors.

“The novelty here is that we take completely different sensors and merge them together on a single small platform as small as a stamp,” said Joseph Wang, a professor of nanoengineering at UC San Diego and co-corresponding author of the study. “We can collect so much information with this one wearable and do so in a non-invasive way, without causing discomfort or interruptions to daily activity.”

The new patch is a product of two pioneering efforts in the UC San Diego Center for Wearable Sensors, for which Wang serves as director. Wang’s lab has been developing wearables capable of monitoring multiple signals simultaneously—chemical, physical and electrophysiological—in the body. And in the lab of UC San Diego nanoengineering professor Sheng Xu, researchers have been developing soft, stretchy electronic skin patches that can monitor blood pressure deep inside the body. By joining forces, the researchers created the first flexible, stretchable wearable device that combines chemical sensing (glucose, lactate, alcohol and caffeine) with blood pressure monitoring.

“Each sensor provides a separate picture of a physical or chemical change. Integrating them all in one wearable patch allows us to stitch those different pictures together to get a more comprehensive overview of what’s going on in our bodies,” said Xu, who is also a co-corresponding author of the study.

Patch of All Trades

The patch is a thin sheet of stretchy polymers that can conform to the skin. It is equipped with a blood pressure sensor and two chemical sensors—one that measures levels of lactate (a biomarker of physical exertion), caffeine and alcohol in sweat, and another that measures glucose levels in interstitial fluid.

The patch is capable of measuring three parameters at once, one from each sensor: blood pressure, glucose, and either lactate, alcohol or caffeine. “Theoretically, we can detect all of them at the same time, but that would require a different sensor design,” said Yin, who is also a Ph.D. student in Wang’s lab.

The blood pressure sensor sits near the center of the patch. It consists of a set of small ultrasound transducers that are welded to the patch by a conductive ink. A voltage applied to the transducers causes them to send ultrasound waves into the body. When the ultrasound waves bounce off an artery, the sensor detects the echoes and translates the signals into a blood pressure reading.

The chemical sensors are two electrodes that are screen printed on the patch from conductive ink. The electrode that senses lactate, caffeine and alcohol is printed on the right side of the patch; it works by releasing a drug called pilocarpine into the skin to induce sweat and detecting the chemical substances in the sweat. The other electrode, which senses glucose, is printed on the left side; it works by passing a mild electrical current through the skin to release interstitial fluid and measuring the glucose in that fluid.

The researchers were interested in measuring these particular biomarkers because they impact blood pressure. “We chose parameters that would give us a more accurate, more reliable blood pressure measurement,” said co-first author Juliane Sempionatto, a nanoengineering Ph.D. student in Wang’s lab.

“Let’s say you are monitoring your blood pressure, and you see spikes during the day and think that something is wrong. But a biomarker reading could tell you if those spikes were due to an intake of alcohol or caffeine. This combination of sensors can give you that type of information,” she said.

Engineering Challenges

One of the biggest challenges in making the patch was eliminating interference between the sensors’ signals. To do this, the researchers had to figure out the optimal spacing between the blood pressure sensor and the chemical sensors. They found that one centimeter of spacing did the trick while keeping the device as small as possible.

The researchers also had to figure out how to physically shield the chemical sensors from the blood pressure sensor. The latter normally comes equipped with a liquid ultrasound gel in order to produce clear readings. But the chemical sensors are also equipped with their own hydrogels, and the problem is that if any liquid gel from the blood pressure sensor flows out and makes contact with the other gels, it will cause interference between the sensors. So instead, the researchers used a solid ultrasound gel, which they found works as well as the liquid version but without the leakage.

“Finding the right materials, optimizing the overall layout, integrating the different electronics together in a seamless fashion—these challenges took a lot of time to overcome,” said co-first author Muyang Lin, a nanoengineering Ph.D. student in Xu’s lab. “We are fortunate to have this great collaboration between our lab and Professor Wang’s lab. It has been so fun working together with them on this project.”

Next Steps

The team is already at work on a new version of the patch, one with even more sensors. “There are opportunities to monitor other biomarkers associated with various diseases. We are looking to add more clinical value to this device,” Sempionatto said.

Ongoing work also includes shrinking the electronics for the blood pressure sensor. Right now, the sensor needs to be connected to a power source and a benchtop machine to display its readings. The ultimate goal is to put these all on the patch and make everything wireless.

“We want to make a complete system that is fully wearable,” Lin said.

Source: EP&T

The COVID-19 pandemic has put a spotlight on drug discovery, which encompasses microscopic viewing of molecules and proteins, sorting through millions of chemical structures, in-silico methods for screening, protein-ligand interactions, genomic analysis, and assimilating data from structured and unstructured sources.

The typical drug discovery process takes about a decade, costs $2bn and suffers a 90% failure rate during clinical development. But the rise of digital data in healthcare in recent years presents an opportunity to improve those statistics with AI.

How AI can reduce drug discovery timelines

Today, we can produce more biomedical data in about three months than the entire 300-year history of healthcare. This is now becoming a problem as no human can really synthesise that level of data, and thus the industry needs to call upon artificial intelligence (AI).

AI is the most powerful technology force of our time. It is software that writes software that no humans can. Researchers worldwide are racing to find effective vaccine and drug candidates to inhibit infection with and replication of SARS-CoV-2, the virus that causes COVID-19.

Graphic Card Units (GPUs) are accelerating this lengthy discovery process – whether for structure-based drug design, molecular docking or generative AI models, virtual screening or high-throughput screening.

To develop an effective drug, researchers have to know where to start. A disease pathway – a chain of signals between molecules that trigger different cell functions – may involve thousands of interacting proteins.

Genomic analyses can provide invaluable insights for researchers, helping them identify promising proteins to target with a specific drug.

With genome analysis toolkits, researchers can sequence and analyse genomes up to 50x faster. Given the unprecedented spread of the COVID-19 pandemic, getting results in hours versus days can have an extraordinary impact on understanding the virus and developing treatments.

Hundreds of institutions, including hospitals, universities and supercomputing centres across the world, are using this kind of software to accelerate their work – to sequence the viral genome itself, as well as to sequence the DNA of COVID-19 patients and investigate why some are more severely affected by the virus than others.

But AI works best when it is domain-specific, combining data and algorithms tailored to a specific field like radiology, pathology or patient monitoring.

Application frameworks bridge this gap by providing researchers and clinicians the tools for GPU-accelerated AI in medical imaging, genomics, drug discovery and smart hospitals.

Healthcare ecosystem rallies around AI

Accelerated computing spending within healthcare is growing at a rapid pace, driven by the increasing computational demand for AI in areas of drug discovery, genomics and imaging.

Amid the COVID-19 pandemic, momentum around AI for healthcare has accelerated, with start-ups estimated to have raised well over $5bn in 2020.

We are seeing more healthcare start-ups than ever harness the power and support of established accelerator programmes too, with record numbers of AI healthcare papers being submitted showing the exponential growth over the past decade.

Leading research institutions like the University of California in San Francisco are also using GPUs to power their work in cryo-electron microscopy, a technique used to study the structure of molecules – such as the spike proteins on the COVID-19 virus – and accelerate drug and vaccine discovery.

And pharmaceutical companies, including GlaxoSmithKline, and major healthcare systems, like the UK’s National Health Service (NHS), will harness technologies like the Cambridge-1 supercomputer – the UK’s fastest AI supercomputer – to solve large-scale problems and improve patient care, diagnosis and delivery of critical medicines and vaccines.

The clinical community is using federated learning approaches to build robust AI models across various institutions, geographies, patient demographics and medical scanners. The sensitivity and selectivity of these models are outperforming AI models built at a single institution, even when there is copious data to train with.

As an added bonus, researchers can collaborate on AI model creation without sharing confidential patient information. Federated learning is also beneficial for building AI models for areas where data is scarce, such as for paediatrics and rare diseases.

In the UK, King’s College London and Owkin are creating a federated learning platform for the NHS. The Owkin Connect platform enables algorithms to travel from one hospital to another, training on local data sets.

It provides each hospital with a blockchain-distributed ledger that captures and traces all data used for model training.

The project is initially connecting four of London’s premier teaching hospitals, offering AI services to accelerate work in areas such as cancer, heart failure and neurodegenerative disease, and will expand to at least 12 UK hospitals in 2021.

Software-defined instruments link AI innovation and medical practice

Software-defined instruments, devices that can be regularly updated to reflect the latest scientific understanding and AI algorithms, are key to connecting the latest research breakthroughs with the practice of medicine.

AI, like the practice of medicine, is constantly learning. We want to learn from the data and we want to learn from the changing environment.

By making medical instruments software- defined, tools like smart cameras for patient monitoring or AI-guided ultrasound systems can not only be developed in the first place, but also retain their value and improve over time.

UK-based sequencing company Oxford Nanopore Technologies is a leader in software- defined instruments, deploying a new generation of DNA sequencing technology across an electronics- based platform.

Its nanopore sequencing devices have been used in more than 50 countries to sequence and track new variants of the virus that causes COVID-19, as well as for large-scale genomic analyses to study the biology of cancer.

The company uses GPUs to power several of its instruments, from the handheld Point of Care MinION Mk1C device to its ultra-high throughput PromethION, which can produce more than three human genomes’ worth of sequence data in a single run.

To power the next generation of PromethION, Oxford Nanopore is also adopting new technologies to enable its real-time sequencing technology to pair with rapid and highly accurate genomic analyses.

For years, the company has been using AI to improve the accuracy of base calling, the process of determining the order of a molecule’s DNA bases from tiny electrical signals that pass through a nanoscale hole, or nanopore.

This technology truly touches on the entire practice of medicine, whether COVID epidemiology or in human genetics and long-read sequencing.

Through deep learning, the base calling model is able to reach an overall accuracy of 98.3%, and AI-driven single nucleotide variant calling gets them to 99.9% accuracy.

The path forward for AI-powered healthcare

We are also seeing the emergence of platforms that use intelligent video analytics and automatic speech recognition technologies to help a new generation of smart hospitals perform vital sign monitoring while limiting staff exposure.

These application frameworks facilitate a critically needed ecosystem of AI solutions for hospital public safety and patient monitoring by transforming everyday sensors into smart sensors.

Critical use cases included automated body temperature screening, protective masks detection, safe social distancing and remote patient monitoring.

Partners across the ecosystem are using pre- trained models and transfer learning to develop and deploy AI applications that combine speech, vision and natural language processing.

AI-powered breakthroughs like these have grown in significance amid the pandemic. The tremendous focus of AI on a single problem in 2020, like COVID-19, really showed that with that focus, we can see every piece and part that can benefit from artificial intelligence.

What we have discovered over the last 12 months is only going to propel us further in the future. Everything we’ve learned is applicable for every future drug discovery programme there is.

Source: PMLive

The notion of 3D printing human organs has been around for a while now. Just last year, after seeing one of my previous articles on additive manufacturing (AM), one of my astute readers asked me when organ printing might become a reality. I had to reply that I didn’t know.

I still don’t, but I have a better answer now: probably in the next several years, thanks in part to the progress made by 3D Systems in its collaboration with United Therapeutics Corporation (UT) and Lung Biotechnology PBC, their organ manufacturing subsidiary. 3D Systems announced a few weeks ago that they’re significantly expanding their investments and efforts in regenerative medicine based on new breakthroughs in bioprinting for lung replacement.

3D Systems has been on a tear in the new calendar year, with its stock rising by over 300% after its estimated 4th quarter revenue of $170 to $176 million beat analysts’ $140 million estimate, and on the heels of its announcement in early January of the sale of two software businesses. Now that announcement late last month has added to those tailwinds. UT, meanwhile, has seen its stock price rise as well, though less meteorically. It had already been rising fairly steadily and substantially through last year despite headwinds from Covid-19 causing mixed business results (earnings were down a few percentage points, but adjusted EPS were up slightly). UT’s share price rose about 75% (though admittedly from something of a trough) through all of 2020, and is now up another 6% this year.

One key driver for the decision by 3D Systems to expand their work in non-solid organ and tissue generation is the tremendous progress they’ve made in printing solid-organ scaffolds for lungs. It represents a big breakthrough in work that’s been underway for some time. “We started the program a few years ago with UT,” said Chuck Hull, who invented stereolithography, founded 3D Systems, and now serves as their CTO. “Their CEO, Martine Rothblatt, is a true visionary who was one of the founders of Sirius XM, then founded UT and asked, ‘Can we produce transplantable lungs?’ She looked at that a variety of ways, and proposed a solution to me: ‘Can you build scaffolds that we can perfuse with lung cells and grow complete lungs?’ At the time I said no, we can’t.”

But it didn’t end there. “I thought about her question for a year, and went back to Martine and said I think we can do it,” Hull continued. “We put a team together between the two companies. There was a lot to work out. We decided to print the scaffolds with hydrogels, because the body is mostly water—but that meant printing a material that’s gooey and soft. We’re used to printing in hard plastics. Once we got that figured out, then we knew the cells needed someplace to grow. That meant printing in fine detail to allow spaces for cells to live in. But printing smaller, with high resolution, slows things down, so then we needed higher print speeds. Those things have taken the last three years to figure out, and there’s still clinical trials and regulatory work to be done—but on the technology side of the scaffolds themselves, I’d say we could implement that now.”

The success in printing that unique structure is a milestone in a number of ways. “Imagine a very fine, highly detailed sponge-like structure with wall thicknesses a fraction of the diameter of a human hair, but yet strong enough to support cell growth and blood flow needed to sustain life,” said Dr. Jeffrey Graves, President and CEO of 3D Systems. “It allows for the creation of organs using actual human cells, which will reduce the chance the body rejects the transplant. The structure is designed and created to allow vascularization, so there’s plenty of blood flowing to the tissues. That’s what we’ve achieved with UT. What Chuck and the team have accomplished is nothing short of amazing. The potential for positively impacting humanity is remarkable. Outside of solid organs, think about replacement tissue for trauma patients, or tissue to allow for better breast reconstruction for cancer survivors. Having reached this state of the technology, there’s now a whole range of human applications, and we’re looking at developing other partnerships around those, while we continue our work with United Therapeutics on lungs and potentially other solid organs.”

Beyond the direct human applications, Graves sees research possibilities as well. “Another potential application is referred to as ‘tissue on a chip,’” he said. “That would produce better testing devices for drugs and other therapeutic purposes, such as cancer treatments. This could help accelerate that kind of development, and could reduce the need for animal testing as well.”

It’s interesting to contemplate that, as an inventor of one of the original 3D printing technologies, Hull helped launch an entire industry a few decades ago. Now his leadership of this bioprinting collaboration is not only accelerating the medical side of the 3D Systems business, but more importantly is also forging a new industrial path for revolutionary healthcare applications of AM.

“I think the takeaway is that we’re well on our way to having implantable tissues,” said Hull. “But that doesn’t happen fast. Long-term, I definitely see a business around this. Short-term, we see steadily increasing revenues.”

“While the technology itself is amazing in its potential benefits to humanity, the business model for 3D Systems is also exciting, and one that is not new to the company,” Graves said. “For years we have developed and produced advanced medical products for the human body using additive manufacturing in a highly disciplined, process-controlled and FDA-regulated environment. With this foundation, if you look at the progress we have now made in all of the essential hardware, software and materials elements of bioprinting, we have the opportunity as a company to address a variety of applications within the human body. That’s why we’re increasing our funding and investment and expanding our partnerships to make these applications a reality. When you see this tremendous scaffold printed, and then you see blood flowing through it that could sustain human life, it’s remarkable, even for someone from an R&D background like me. I am continually amazed at the potential this technology now offers.”

Graves sees the organs and tissues work as a natural extension of what’s already a strong business for 3D Systems. “In terms of the impact on our company, our healthcare business is growing nicely, now approaching half of the company revenue,” he said. “We believe that regenerative medicine will add to this growth, becoming a significant business in its own right in several years. This technology offers tremendous benefits for human life, whether helping people whose lungs or kidneys are failing, or offering remedies for someone who’s experienced facial or other traumas in their body. It’s sad when you think of the tremendous shortage of replacement organs today and that the primary supply originates from the loss of another person’s life. The only way to address these needs is through an alternative source of supply, and hopefully one that offers greater compatibility with the recipient of the new organ or tissue implant. With the progress we are now making, we believe that regenerative medicine, and the underlying bioprinting technology we have now demonstrated, can ultimately be an answer to this problem.”

All that being said, though, I still don’t have the specific answer for the question from the reader of last year’s article I mentioned above. But I do have this:

“I’m guessing some of your readers will be saying, ‘I have such-and-such condition—how soon will this solve it?’ Hull said. “And my answer is, as quick as we can.”

Source: Forbes

Drug development takes a long time, most of which is taken up by testing.

Before reaching humans, new medicines are first tested on mice, then rats, dogs and primates. “It can sometimes take 15 to 20 years for a drug discovery to happen,” said Dr Deepak Kalaskar, associate professor at University College London. “The reason is these animals do not replicate human genomics or physiology.”

In the future, said Kalaskar, pharmaceutical companies will bypass all those time-consuming pre-human stages. The enabling technology is bioprinting – 3D printing of living cells and other materials to create biological tissue and organs.

By using human cells in printers such as the NovoGen Bioprinter from Organovo, researchers can replicate ‘targeted’ tissues throughout the body, such as skin or liver tissue. They are exposed to viral particles, bacteria and drugs before microscopic and other observation.

Bioprinting made headlines last year after projects including the development of ‘organoids’ at the Wake Forest Institute for Regenerative Medicine in North Carolina. Led by Dr Anthony Atala, the team built miniature lungs and colons to assist Covid-19 research, the New York Times reported. Constructed by using a scaffold of biodegradable material followed by a ‘bioink’ of cells and hydrogels, the organoids were used for drug testing.

Off-the-shelf organs

“If you have an off-the-shelf organ ready, we are not exposing these pathogens to healthy volunteers,” said Kalaskar. “We will be able to do this in a very safe manner, under laboratory conditions, and we will still get our relevant human information.”

The main challenge to creating fully functional human organs is keeping the tissues alive. In the body, networks of capillaries distribute oxygen and nutrition throughout tissues. Kalaskar and colleagues are developing blood vessel structures in the lab, aiming to perfuse blood through artificial tissue. The next stage is scaling up the process and ensuring it can be embedded within the printed tissue, before checking long-term survival.

Other teams have taken different approaches. In November 2020, bioengineers at Harvard Medical School in Massachusetts created liver-like tissue by combining human cells, a hydrogel matrix and photosynthetic algae.

“The study is the first true example of symbiotic tissue engineering combining plant cells and human cells in a physiologically meaningful way, using 3D bioprinting,” said senior study author Y Shrike Zhang. The algae provided a sustainable source of oxygen for the human cells, while they provided carbon dioxide.

The tissues could be used for drug testing – and eventually might be implanted for tissue regeneration within patients.

Source: imeche.org

Researchers at Geisinger have found that a computer algorithm developed using echocardiogram videos of the heart can predict mortality within a year.

The algorithm--an example of what is known as machine learning, or artificial intelligence (AI)--outperformed other clinically used predictors, including pooled cohort equations and the Seattle Heart Failure score. The results of the study were published in Nature Biomedical Engineering.

"We were excited to find that machine learning can leverage unstructured datasets such as medical images and videos to improve on a wide range of clinical prediction models," said Chris Haggerty, Ph.D., co-senior author and assistant professor in the Department of Translational Data Science and Informatics at Geisinger.

Imaging is critical to treatment decisions in most medical specialties and has become one of the most data-rich components of the electronic health record (EHR). For example, a single ultrasound of the heart yields approximately 3,000 images, and cardiologists have limited time to interpret these images within the context of numerous other diagnostic data. This creates a substantial opportunity to leverage technology, such as machine learning, to manage and analyze this data and ultimately provide intelligent computer assistance to physicians.

For their study, the research team used specialized computational hardware to train the machine learning model on 812,278 echocardiogram videos collected from 34,362 Geisinger patients over the last ten years. The study compared the results of the model to cardiologists' predictions based on multiple surveys. A subsequent survey showed that when assisted by the model, cardiologists' prediction accuracy improved by 13 percent. Leveraging nearly 50 million images, this study represents one of the largest medical image datasets ever published.

"Our goal is to develop computer algorithms to improve patient care," said Alvaro Ulloa Cerna, Ph.D., author and senior data scientist in the Department of Translational Data Science and Informatics at Geisinger. "In this case, we're excited that our algorithm was able to help cardiologists improve their predictions about patients, since decisions about treatment and interventions are based on these types of clinical predictions."

The research was supported in part by funding from the Pennsylvania Department of Health and the Geisinger Health Plan and Clinic.

About Geisinger

Geisinger is committed to making better health easier for the more than 1 million people it serves. Founded more than 100 years ago by Abigail Geisinger, the system now includes nine hospital campuses, a 550,000-member health plan, two research centers and the Geisinger Commonwealth School of Medicine. With nearly 24,000 employees and more than 1,600 employed physicians, Geisinger boosts its hometown economies in Pennsylvania by billions of dollars annually. Learn more at geisinger.org or connect with us on Facebook, Instagram, LinkedIn and Twitter.

Source: EurekAlert!

“Hi Tom, this is Chris! You said you were feeling feverish yesterday. I wanted to know if you are doing better. On a scale of 1-10, how would you rate your symptoms today?”

Who do you think that was? That sure wasn’t a doctor, that was an AI-driven chatbot.

COVID-19 changed a lot of aspects of our daily life. Among many changes, noticeable changes reflected in the healthcare segment. An industry that faces challenges every day, had to take a long leap to adjust to the drastic effects of this pandemic. Globally, two things emerged – telemedicine and chatbots. While doctor consultations via call existed to a certain extent pre-COVID, telemedicine with the inclusion of AI has now become primary care.

How AI Chatbots Are Transforming Healthcare

Artificial Intelligence-enabled chatbots are providing a new approach for patients to receive the right healthcare at the right time. By now, you must have seen chatbots pop when a website opens. Those bots ask basic questions like “how may I help you” or “what are you looking for”. But in the medical sector, that is not it. Chatbots take various forms. They perform various tasks like scheduling appointments, billing, patient engagement.

AI chatbots mimic human conversation through text chats (commonly) and voice commands. But unlike humans, they can be available 24/7 in any geographical location. Most often, chatbots come with preloaded FAQs and answers that are programmed to adapt to human responses. Through this loop of conversation, healthcare providers are receiving tons of patient data that creates new opportunities.

Nick Desai, CEO of Heal, a telemedicine platform calls this digital primary care. He says “there is still an irreplaceable value to the human-doctor patient interaction. What we want to do is give doctors data-driven decision support.”

How Is Data-Driven Support Helping Patients & Hospitals?

• For patients dealing with chronic conditions, a short response time from health care providers is crucial. Here, chatbots help doctors from getting burnt out. Health care providers can speak to their AI-powered devices to record notes, schedule appointments, order medicines from pharmacies, and generate reports.

One such platform is SafedrugBot that mediates messages between breastfeeding patients and doctors via the Telegram app. This app helps health care providers get updated information about the patients and prescribe them pregnancy-safe medicine. This app also helps patients by giving them alternate names to a specific doctor-prescribing medicine.

• What happens when you read about cold symptoms on websites like WebMD? The answer might be more gruesome than needed. That’s probably why doctors stop people from searching about their symptoms on the internet. But these days, there are chatbots that will give a much accurate answer, thanks to databases of medical information and similar patient experiences.

One such app is Ada. Ada is a personal health companion that uses AI to determine a patient’s symptoms by asking questions like “Where does it hurt?” to which it gives a diagnosis based on several GBs of medical data. The advantage of using such a platform is that it learns more about the patient with each interaction, thanks to AI. About the diagnosis, Ada generates a report that the patient can show the doctor.

• For patients who juggle a million different tasks daily, chatbots can become their personal nurses. It can help schedule and remind follow-up checkups, send out reminders to take medicine, and answer quick FAQs.

LifeLink powers chatbots that provide patients with accurate wait times, give an understanding of medical procedures, assists in finding doctors and scheduling appointments, sends families live updates about the patient’s health, and informs patients about tests. Such apps increase patient engagements and reduce uncertainties and frustrations.

For health care providers and patients, this AI-driven chatbot technology is a win-win. These chatbots hold vast capabilities that can make healthcare a rather easy and less-intimidating aspect of people’s lives. It helps patients find doctors, schedule appointments, get details about tests and procedures, and medical bills. Chatbots increase patient engagement and patient satisfaction which is the ultimate goal of healthcare.

Source: Analytics Insight

Medical-related 3D printing has come a long way especially in producing organs. What once seemed like science fiction has become reality and the healthcare industry is better for it.

But what about 3D printing bones? Back in 2016, we reported on the work of some researchers at Northwestern University, Illinois, that had 3D printed a scaffolding material that combined hydroxyapatite, a mineral found in bone, with polycaprolactone, a biocompatible polymer.

The end result was a bone replacement that the body did not reject. Since then, however, we heard little about 3D printed bones. Now, a team at the University of New South Wales (UNSW) in Sydney, Australia, has engineered a ceramic ink that can be 3D-printed with live cells and without the dangerous chemicals often associated with this process.

The researchers are even claiming that it could allow the bones to be 3D printed directly into the human body. “In contrast to previous materials, our technique offers a way to print constructs in situ which mimic the structure and chemistry of the bone,” said study co-author Iman Roohani, a bioengineer at UNSW's School of Chemistry.

Currently, the most common method for repairing bones is autologous (meaning from the self) bone grafting. However, these grafts have high rates of infection and simply don’t work if the bone material needed is too big.

Therefore, UNSW researchers came up with ink that could be 3D printed into an aqueous environment that mimics the human body. Their ink takes the form of a paste at room temperature, but once put into a gelatin bath, it hardens into a nanocrystal matrix similar to the structure of real bone tissue.

The team is now attempting to print large structures and testing on animals to see how effective their 3D printed bone parts are. The study is published in the journal Advanced Functional Materials.

Source: Interesting Engineering

This month’s mobile health apps round-up features news of a 25% increase in the use of health apps and Garmin launching a wellbeing app for employees.
Use of health apps ‘explodes’ during coronavirus

App evaluation company ORCHA has reported an “explosion” of digital healthcare tools since the outbreak of Covid-19.

Since the start of the pandemic there has been an 25% rise in health app downloads, totally five million downloads a day.

Key findings of the Covid-19 Digital Health Trends report:

• Downloads of apps supporting consumers with mental health needs increased by nearly 200% from summer 2019 to summer 2020

• Downloads of those supporting consumers with diets and weight loss rose by a massive 1294% from mid-2019 to mid-2020

• And downloads of apps helping consumers manage their diabetes rose by 482%

Liz Ashall-Payne, chief executive of ORCHA, said: “Covid-19 has seen a massive upsurge in the use of health apps. Using our ORCHA app libraries, thousands of GPs and health teams are now recommending apps – more than ever before.

“With NICE having introduced guidance on digital health, 2021 will be the year when health and care staff embrace the full potential of apps – and it goes way beyond video conferencing.

“We will start to see digital approaches being integrated into care pathways. For example, an app like FibriCheck will allow patients to check their heart rhythms, using a medically certified system. They’ll be able to detect arrhythmias and avoid complications like stroke.

“Another example, Vinehealth, which is approved by the NHS, helps cancer patients understand their treatment – and evidence shows that this leads to less emergency room visits.”

JotForm launches app for safe collection of patient data

Online form builder JotForm has launched JotForm Health app – a tool that allows healthcare providers to securely collect medical data anywhere.

The JotForm Health app is an extension of JotForm’s web-based online form builder. Once a form is submitted through the app, JotForm automatically encrypts the data, guaranteeing the privacy of health information.

Aytekin Tank, founder and chief executive of JotForm, said: “From frontline workers screening for Covid-19 to local governments collecting vaccine registrations, the JotForm Health app removes barriers to information collection.

“It empowers doctors, nurses, and other frontline workers to gather important data on- or offline from any device.

“The pandemic has revealed inefficiencies in traditional ways of collecting data, and the JotForm Health app is an elegant solution. It helps with conducting Covid tests, doing effective contact tracing, and rolling out the vaccines.”

The app is includes on and offline data collection, an e-signature field, team collaboration functions and is HIPAA compliant.

Garmin introduces health and wellbeing app for employees

Fitness tech company Garmin has introduced a health and wellbeing app for its 800 employees to support them during the pandemic.

The app will be available from January 2021 and offers a range of personalised and interactive health plans, such as stress and heart rate advice.

Employees will also have access to podcasts, videos and articles to help them better manage their fitness, Employee Benefits reported.

Jörn Watzke, senior director global business development at Garmin Health, said: “Finding new and impactful ways to maintain a healthy workforce is a challenge, and even more so when teams are working remotely or in different countries, so to find the right solution for this was critical to us.

“Alongside having a platform that delivers a seamless user experience, it also creates bespoke, engaging and differentiated content that will truly help our employees to meet and exceed their health and wellbeing goals.”

Source: digitalhealth

The team found that post-traumatic stress disorders and sexual assault trauma were associated with functional seizures.

Vanderbilt University Medical Center researchers used electronic medical record data to determine the prevalence of "functional seizures" and the comorbidities associated with them.

In a study published this past week in the Journal of the American Medical Association, the team found that post-traumatic stress disorders and sexual assault trauma were associated with functional seizures, or episodes that are similar to epileptic seizures in their clinical presentation but display no aberrant brain electrical patterns. Functional seizure patients face an average diagnostic delay of seven years.

"I felt like studies within electronic health records could potentially be really impactful for this community," said Lea Davis, who headed the team, to the VUMC Reporter.

WHY IT MATTERS

Using a database of deidentified EHR data from VUMC, researchers developed a clinically validated phenotyping algorithm to identify functional seizures cases; estimated the period prevalence of functional seizures in a hospital population; and identified comorbidities associated with functional seizures.

The study population included more than two million VUMC patients from 1994 to 2019, with the team extracting demographic characteristics, ICD-9 and ICD-10 codes, CPT codes and clinical notes from the EHR and mining them for analyses.

"Based on the number of patients identified by our algorithm in proportion to the total number of patients in our hospital system, we calculated the period prevalence of functional seizures to be 0.14% in our clinical population," wrote researchers.

The team found evidence supporting existing reports that functional seizures co-occur with psychiatric and neurological disorders.

In addition, patients with functional seizures are nearly 16 times more likely than the average hospital patient to have a documented history of sexual assault trauma – and that such trauma explains 22% of the increased rate of functional seizures in women.

The results suggest that functional seizure patients are at risk for additional chronic health conditions, including cerebrovascular disease.

"However, we observed no clear illness trajectory from functional seizures to cerebrovascular disease, and in fact found that cerebrovascular disease often preceded the onset of functional seizures," researchers noted. "These findings have important implications for the management of patients who develop post stroke seizures."

THE LARGER TREND

Given the large volumes of information available in EHRs, researchers and software companies have developed new tools for more easily extracting data.

For instance, researchers from the University of Michigan last year developed an open-source framework that streamlines the preprocessing of EHR data.

"By accelerating and standardizing the labor-intensive preprocessing steps, FIDDLE can help stimulate progress in building clinically useful [machine learning] tools," wrote those researchers.

ON THE RECORD

"Overall, we believe that this novel EHR-based study provides important rationale and motivation for ongoing EHR-based research to improve the complex and challenging clinical care of patients with functional seizures," said the VUMC team.

Source: Healthcare IT News

The pharmaceutical industry has long relied on cutting edge technologies to help deliver safe, reliable drugs to market. With the recent pandemic, it’s proved more important than ever for pharmaceutical companies to get drugs and vaccines to market faster than ever before.

Artificial intelligence and machine learning have been playing a critical role in the pharmaceutical industry and consumer healthcare business. From augmented intelligence applications such as disease identification and diagnosis, helping identify patients for clinical trials, drug manufacturing, and predictive forecasting, these technologies have proven critical. On a recent episode of the AI Today podcast Subroto Mukherjee, who is Head of Innovation and Emerging Technology, Americas at GlaxoSmithkline Consumer Healthcare discussed how AI and ML are being applied to the pharmaceutical industry and some unique use cases for AI and ML technology. In this follow up interview he shares his insights in more detail.

How is AI currently being applied in the pharmaceutical industry?

Subroto Mukherjee: AL and ML have been critical in the pharmaceutical industry and consumer healthcare business. AI and ML are playing an important role during this pandemic, driven by COVID and the race to discover effective vaccines. The top-level uses in Pharma and Consumer Healthcare arena as follows:

• Disease Identification/Diagnosis – It can range from oncology to Covid to degeneration in the eyes.

• Digital Therapeutics / Personalized Treatment/Behavioral Modification – This can be effectively used to assist and identify individuals to provide early insight into the condition – such as gum condition, accurately classify cutaneous skin disorders, suggest primary treatment options with Over-the-counter medication, and serve as an ancillary tool to enhance the diagnostic accuracy of clinicians, or improve educational and clinical decisions made by your child's teacher, or your mental health professional or even your medical doctor.

• Drug Discovery and Manufacturing: It helps in the initial screening of drug compounds to the predicted success rate based on biological factors. Measuring RNA, DNA quickly. Precision medicine or next-generation sequencing helps in the faster discovery of drugs and tailored medication for individual patients.

• Predictive Forecasting – Predicting an epidemic is one of the key examples of this topic. ML and AI technologies are also being applied to monitoring and predicting epidemic outbreaks or seasonal illnesses worldwide. A predictive forecast helps plan our supply chain to get the inventory at the right time and the right quantity based on the predicted intensity.

• Clinical Trials – Identifying the right candidate for the trial based on history and disease conditions, and additional attributes, overlaying with infection rates, demographics, and ethnicity to represent the most impacted.

Apart from the Healthcare conditions, we see many AI ML usage in Digital Transformation areas for Pharma and Healthcare companies such as Martech, AdTech, Supply Chain, Sales, and Customer Service.

What are some unique use cases for AI and ML technology in the pharmaceutical industry?

Subroto Mukherjee: As per the article in guardian-Artificial intelligence group, DeepMind has cracked a serious scientific problem that has stumped researchers for half a century. AlphaFold, the company and research laboratory using the AI program, showed it could predict how proteins fold into 3D shapes. The advantage of this discovery is that it will help researchers discover the mechanisms that drive some diseases and pave the way for - designer medicines, more nutritious crops, and "green enzymes" that can break down plastic pollution.

Another unique case and my favorite and involved in enabling the GSK consumer R&D team is AI in Sensory Science. AI and ML are ramping up predicting parameters in foods, beverages, agriculture, and medicine. This could lead to hyper-personalized products for food, beverage, and medicines customized for different demographics and ethnicities; we extensively use sensory properties beyond taste, such as smell, appearance, and texture, influencing what we select to eat or drink.

Can you share use cases where AI was successfully applied at GlaxoSmithKline?

Subroto Mukherjee: Let me share some use cases in our consumer healthcare line of business.

Predictive Forecasting: We have popular seasonal brands in the Allergy and Cold and Flu category. The business use case is to have a predictive model that predicts how the upcoming season for allergy or cold and flu would shape up in different regions, and when are the predicted peaks and troughs. The advantage of this information is to inform consumers on our brand.com website, improve our national and regional media delivery and inform retailers of seasonal activation timing (distribution, stock up, display and secondary support).

Sensory Models – Humans react differently, to taste, size, texture, color, and Sensory AI models help in a holistic way of understanding, predicting, and optimizing consumer preference. We use multiple parameters, such as taste, texture, color, and ML models, to understand the relationship between the consumer and the desired product experience. Our brands offer gummies, tablets, and liquids for our over-the-counter products, and these models are beneficial.

AI in eye-tracking: We do studies with our consumers and retailers in our shopper’s science lab and monitor how they look at our products while they shop online or in stores. Consumers and retail teams with consent in our labs wear eye-tracking glasses and look at the products on shelf or online. During this process, images are captured and analyzed using AI. The analysis includes Areas of Interest (AOI) metrics, including the time to first fixation and time spent, gaze plots, heatmaps, and video replays. This helps in better product placement, improves our art and labeling, and helps us understand consumer behavior.

What are some of the challenges to AI adoption at larger organizations?

Subroto Mukherjee: Key challenges to AI adoption at larger organizations are as follows:

Data Challenges – Quality and quantity of data. As for any machine learning model to work efficiently, a training data set with a minimum of 2 to 3 years of historical data is critical. This is the most critical challenge we see in large organizations due to mergers and acquisitions or prior data management or prior source of data being unavailable.

Skills Challenges – Getting the right resource and with the right background is very challenging. We have a limited data science skilled pool in the market, delays hiring and getting them up to speed and scale multiple AI projects.

Business Value – Larger organizations are struggling to prove the business value for AI projects. For example, we would like to deploy more cognitive services based on chatbots. Still, adaptability is not significant and results in difficulty in proving the value out of such endeavors.

What are some of the challenges around data privacy, security, ethics, and transparency that organizations such as GSK are dealing with?

Subroto Mukherjee: Data privacy and security are of the highest importance for our organization. We constantly ensure all data privacy, security laws are followed, and appropriate training is provided across our different portfolios and adhered to by our partners and complementary workers. Data classification (PII, CSI, Sensitive), adherence of our systems, and processes to the GDPR or California privacy rights act's needs are some of the challenges we constantly face.

For AI ethics and transparency, we make sure MLOps processes are in place, and Machine learning (ML) models model scoring is established, monitoring and drift detection, the feedback loop is transparently followed. We bring a diverse ML team with diverse experience embedded in the team and test the models constantly to bring transparency and remove bias from the Machine learning models.

The global pandemic has really shaken up the pharma industry. How are you seeing AI and machine learning being put to use in the fight against the pandemic?

Subroto Mukherjee: Concerning the pandemic – the biggest use of AI and machine learning from my understanding is to tease out COVID's biological secrets and identify the few molecules which will help end COVID among the millions and to reduce the time to market drugs – either be discovery, development to clinical trials and final FDA approvals. Look at the speed and agility of the current vaccine – it took 300 days from identifying the coronavirus genome to the first vaccine study, which has previously taken an average of eight to ten years.

Medical Mining - Let me focus on one specific initiative - "US White House - Call to Action." to analyze and Transform COVID-19 Data into Clinical Knowledge. White House is partnering with the AI research community to understand the novel coronavirus by mining medical literature. Natural language processing is one of the fastest-growing practices in this area, helping with this initiative. Medical imaging companies using AI and ML claimed record-level accuracy in detecting covid-induced pneumonia from CT scans, despite concerns from some stakeholders on the quality of training data.

Another important impact of COVID-19 is the impact of the supply chain. All companies, including ours, are facing the impact of COVID in the supply chain and manufacturing. Be it the supply of raw material or distribution of finished goods, it helps in pre-empting the risks associated with it. Companies are scrambling to respond to rapidly shifting consumer demand, limited supply of some products, and new workplace rules. AI and ML are used in Planning and Forecasting, Bots for automation and collaboration, and many key areas of the value chain.

How do large organizations approach change management for transformative technologies such as AI?

Subroto Mukherjee: We are implementing agile transformation across the business to create an effective and simple change management structure. Our technology organization, business team, and leadership team have undergone agile training. The change management discipline has been re-oriented with a clear hierarchy of approvals (key decision-makers) for onboarding new AI technology solutions. We define clear business objectives and value for now, next, and later for these transformative technologies.

What do you see as critical needs for workforce development around AI?

Subroto Mukherjee: We need reskilling and education among the workforce, not only in technical aspects but also in AI's business value. AI for Good or AI ethics is another key aspect that employees and the business community need to understand. Workers should not be afraid of AI, but rather embrace it and understand the benefits of AI. In terms of workforce, organizations need to scale up slowly with monitored results and a pool of data scientists knowing the business, data engineers, and subject matter experts.

How is the global regulatory environment impacting the pharma industry’s adoption of AI?

Subroto Mukherjee: It is necessary to meet compliance and regulatory requirements as regulators need to safeguard consumers, and it does impact the timelines of new AI solutions to be rolled out. But organizations should be collaborating with regulators to streamline this process to the benefit of all. Both regulators and pharma companies can embrace AI and other digital transformation initiatives to drive the economy, cost efficiency, and value-driven effectiveness of regulatory operations.

What AI technologies are you most looking forward to in the coming years?

Subroto Mukherjee: I am looking forward to the advancement and extended use of Natural language processing, Robotics, Speech, and computer vision in the coming years.

Source: Forbes

Singapore firms have developed digital health passports that can verify travellers’ COVID-19 test results, as the country gradually reopens its borders with safe management measures in place.

Tech firm Affinidi said it is working with government agencies and private sector partners on trials for inbound travellers, while two other companies said they were involved in pilots for the Singapore-Hong Kong air travel bubble, which was supposed to begin on Nov 22 but has since been postponed.

DIGITAL HEALTH PASSPORTS

Digital health passports allow clinics and hospitals to share healthcare data across borders in a secure manner, using technology like blockchain.

After prospective travellers take their COVID-19 test at a healthcare provider working with these apps, developers issue a QR code with the test result, which travellers show to immigration authorities.

When scanned, officers can see details such as whether the laboratory is on the destination country’s whitelist, what type of test was taken, and whether it was done within the required time frame.

“So we actually work directly with the clinics, hospitals and laboratories, where they send data to us securely either through an API or through our cloud-based solution, and we take that data, make it into a verifiable document, and put it into the hands of the individual in their digital health passport,” said Mr Quah Zheng Wei, co-founder and CEO of Accredify.

This is to ensure that test results have not been tampered with while ensuring that sensitive personal health records are shared only with those the user chooses to share them with.

This compares to options like opening up centralised databases of medical records to countries around the world, which brings up issues of data privacy and could potentially make healthcare providers targets for cyberattacks.

ENSURING RELEVANCE WITH WIDE NETWORKS

Accredify has 80 labs and clinics in Singapore and Hong Kong on board, through partnerships with private healthcare providers such as Parkway Pantai and Raffles Medical Group.

Next year, it aims to expand its network to about 440 clinics in Singapore that have been authorised to offer COVID-19 polymerase chain reaction tests, and it is expecting more to come on board as testing gets ramped up.

Another digital health passport, ICC AOKpass, has access to a global network of about 73,000 medical providers that are regularly audited by its partner International SOS.

“It provides a go-to network, especially when we're dealing with business clients, because many of them are already serviced by this network. So the referral to the high quality clinic … that already exists in many ways through a network like that,” says Dr Chester Drum, co-founder of ICC AOKpass.

“That network is present in almost every country. And that's the advantage – if you're in a sparsely populated country in Africa, or if you're in the middle of London, there's access no matter where you go.”

PLUGGING THE INTEROPERABILITY GAP

With at least 10 digital health passports being developed by airlines, multinational organisations and private entities, a key problem will be interoperability – what happens when a traveller turns up at an immigration counter with an app the officer has never seen before?

“There are many providers today that are helping you to issue these test results in a way that is verifiable. The problem then is that there are many different standards, which the immigration officer or the (airline) check-in counter will need to verify against,” said Mr Nicholas Foo, who does business development at Affinidi.

Drawing a comparison with credit card payment systems, Mr Foo said it is unlikely that a single type of digital health passport will dominate the scene. Instead, what is needed is "a single terminal (that) can read everything".

To help do so, Affinidi has built a web application it calls the Universal Verifier.

“We will work with all these different standard providers to be able to read the QR codes and display the result in the same way to the immigration officer. This helps to save a lot of time, and the immigration officer doesn't have to learn different types of standards,” said Mr Foo.

Affinidi said more details about trials in Singapore will be revealed soon. It has also received interest from other countries and is engaging them to see how it can scale up the solution.

“The vision is that we will trial this in the region first, but hopefully expand this to become a more global solution in the near term,” said Mr Foo.

Affinidi currently has eight digital health passport providers on board, including Accredify and AOKpass. Others include Knowledge Catalyst, NextID, Collinson, and 3DCerts.

OTHER APPLICATIONS

According to the solution providers, these platforms have potential beyond international travel.

“For example, when we spoke to counterparts in Malaysia, in Indonesia, there are requirements for a negative COVID-19 test to be proven for you to cross between states,” said Mr Foo.

“Negative COVID-19 test results are also required for the purposes of employment … these are very real-life applications.”

Such systems may also be useful for gaining entrance into events.

Providers said these solutions can also be easily adapted if proof of COVID-19 vaccinations becomes a requirement in future.

“This is ultimately a game of confidence, and the more confidence you have, the more willing you are to open up borders. (Having) the confidence that the people (who) are travelling to your country have these digital certs will allow us to open the borders more quickly,” said Mr Foo.

Source: CNA

Coronavirus has become an uncommon disturbance to all aspects of the medical care industry in a short amount of time. In spite of the fact that the healthcare technology industry has been slow growing previously, development was needed to manage the pandemic. Artificial intelligence in medical care, as well as other significant advances, are critical to settling the crisis and for creating future development.

The technological advancement in the healthcare industry is molding the world to a better future. Advancements, for example, AR/VR, Artificial Intelligence, Robotics, 3D printing, and Nanotechnology are reforming the operations of healthcare companies . 2020 has been challenging for the healthcare industry as an industry with more roles and obligations in the pandemic.

2021 and the situation is as yet unsure regarding the pandemic, however, technological progressions will never stop to facilitate the process and have a better outcome in the healthcare industry. Let’s have a look at innovations that will have an unmistakable impact in 2021.

Telemedicine

Telemedicine, or the act of clinicians seeing patients virtually instead of in physical workplaces and medical clinics, has expanded colossally during the pandemic as populations around the globe have restricted physical experiences. This practice has shown that remote consultations are conceivable, yet in addition easy and often preferable. A few experts state that this is just the start, and soon the size of telemedicine will increase.

Virtual Reality

The patient and doctor’s lives are overhauled with virtual reality. Later on, while you get worked on and to divert the pain patients taken to a vacation location. The development and the effect are failing to meet expectations for virtual reality innovation starting in 2020, yet the coming years will consistently be productive. The technologies are helpful for patients in pain management. Additionally, women are furnished with virtual reality headset to forget the labor pain.

The Internet of Medical Things (IoMT)

Different devices and mobile apps have come to play an important part in tracking and forestalling persistent ailments for some patients and their doctors. By consolidating IoT development with telemedicine and telehealth innovations, a new Internet of Medical Things (IoMT) has arisen. This methodology incorporates the utilization of various wearables, including ECG and EKG monitors. Numerous other medical estimations can likewise be taken, for example, skin temperature, glucose level, and pulse readings.

By 2025, the IoT business will be worth $6.2 trillion. The healthcare services industry has gotten so dependent on IoT innovation in 2020 that 30% of that market share for IoT devices will come from healthcare.

Mobile Health

The quick headways we are finding in the healthcare application space will proceed in 2021 too. Apple has released its open-source programming structures like Carekit and Researchkit which are incredible platforms for application designers to build healthcare based medical apps and contribute to medical research.

There are numerous healthcare engineers for recruitment with core domain expertise. mHealth activities would see better results in the year to accompany the emphasis on giving personalized care to singular patients and utilization of data sharing for research in early diagnosis of illnesses and their therapy.

Healthcare wearable and sensors

Wearables are assisting people with tracking their health details and can take fundamental measures whenever required. The wearables are recording from the heartbeat till your blood pressure to the anxiety. Stress management and sleep monitoring is something each individual is searching for and are working persistently in this bustling life. The sensors are different devices that we will be more educated to individuals and the number of clients will increase by 1,000 folds.

Blockchain

Blockchain is a pattern that stands to unfathomably improve the healthcare industry in 2021 and coming years. Digital ledgers can empower medical services suppliers to disseminate transaction records to patients safely and will extraordinarily improve data security. Blockchain’s peer-to-peer system permits huge quantities of users to safely approach a common ledger. Because of blockchain, there is no requirement for a premise of trust between two parties. As healthcare technology keeps on improving, conveyability, security, and accessibility are totally wanted goals that blockchain can help complete alongside different patterns like IoMT and cloud computing.

Cloud Computing

Data collection and record keeping are an indispensable part of medical services, and truly, the management of this data has consistently been a challenge for healthcare suppliers. Cloud computing in healthcare has become the go-to choice for the management of electronic medical records.

It is worthwhile for both patients as well as healthcare providers as it makes the consultation process more consistent and spares important time. Putting away information on the cloud gives it remote accessibility and facilitates better collaboration.

Nanotechnology

The future in the healthcare industry is about nanotechnology, the headway in nanotechnology will help streamline the treatments. Organizations are giving a nano-pill camera that is utilized to study inside part of the body and assists with treating patients better. The coming years will assist us with better nanotechnology pills, nano-particles will go about as the drug delivery system, especially in treating cancer.

3D-Printing

The future go for medical services is comprehensively observed with 3D printing technology with printing the body tissue till the artificial appendages, veins, pills, and some more. Organizations are delivering skin tissues with the platelets that help in supplanting the skin burn, and other skin related issues faced by the patients. The medications printed from 3D printing advances have been used since 2015 that are endorsed by the FDA.

Big Data and Predictive Analytics

Arrangement and collation of healthcare data which is far-reaching is a challenge in itself. Combination of ground-breaking computing, advanced database technologies and front line analytics software has prompted big data finding its truly necessary application in the field of healthcare.

A tremendous amount of healthcare information is being generated at both organizational as well as individual level going from medical diagnosis and imaging data in medication to observing fitness information. The big data analytics tools and storehouses produce reliable and calculative experiences out of these volumes of information within a very short duration.

Source: Analytics Insight

The Commonpass will record passengers' Covid tests result to enable easy travel

Five leading airlines are rolling out digital health passports so that passengers with a recorded negative Covid-19 test can travel easily and avoid the need to quarantine.

From December 15, people flying with JetBlue, Lufthansa, Swiss International Airlines, United Airlines, and Virgin Atlantic airlines can use the CommonPass to record their Covid status, using it as proof of their Covid status to show to airport staff.

The CommonPass saves the user's test results onto their mobile device, along with any other health screening information mandated by the destination country. The pass then generates a QR code which can be printed or scanned by airline staff to confirm the passenger's health status.

Swiss non-profit The Commons Project Foundation created the digital pass, with backing from the World Economic Forum. This week the two organisations joined forces with Airport Council International (ACI) World, who represent almost 2,000 airports worldwide, along with the five participating airlines.

The CommonPass was first trialled in October on Cathay Pacific Airways and United Airlines flights between Hong Kong, Singapore, London, and New York.

The pass isn't mandatory for travelling, but is the first standardised format for Covid test results to be used this way. It is hoped it will be widely adopted by the public to make travelling easier.

“The recent digital ‘health pass’ trials, such as CommonPass, are presenting a strong case for using digital technology to deliver harmonised standards in the validation and verification of accredited passenger health data,” said a statement from airline alliances, whose 58 member airlines represent over 60 per cent of world airline capacity.

“As the world works to overcome the pandemic, all countries face the challenge of how to reopen borders for travel and commerce while protecting their populations’ health,” ACI World Director General Luis Felipe de Oliveira said. “Key to this will be a globally-harmonised approach underpinned by cooperation and consistency between all players in the aviation industry.”

“The CommonTrust Network and CommonPass will help to foster this consistent approach, especially as it will include more than just the aviation industry."

Source: Healthcare

The De Novo authorization marks the latest in a string of FDA nods for digital therapeutics, a class of devices a sector trade group says leverage software to provide "evidence-based therapeutic interventions" across a range of disease states and stages. Often, they're meant to be used in tandem with other treatments. This past summer FDA OK'd the first video game-based therapeutic, an ADHD treatment from Akili Interactive Labs.

Nightmares have been linked to increased suicidality and risk for heart disease and diabetes, in addition to cognitive difficulties such as memory loss, anxiety and depression. About seven or eight of every 100 people will experience PTSD in their lives, according to the National Center for PTSD, a U.S. Department of Veterans Affairs program. People who have experienced or witnessed a dangerous or traumatic incident can develop physical and cognitive symptoms.

Nightware's personalized treatment for nightmare disorder and nightmares related to PTSD detects the sleep disturbance at its onset and intervenes using vibrations to arouse the sleeper without waking them. The patient's nightmare is interrupted but circadian sleep pattern continues, allowing more restful sleep, according to the company.

The three-year-old company's smartphone application gained FDA's breakthrough device designation in May 2019. The process aims to speed development and review of devices with potential to improve treatment of serious or life-threatening conditions when there is no approved therapy offering similar benefits.

FDA reviewed the Nightware product through the De Novo premarket review pathway for low- to moderate-risk devices. The agency said it is establishing special controls to provide reasonable assurance of safety and effectiveness for such devices that include labeling and performance testing requirements.

The decision also establishes a new regulatory classification for prescription digital therapy devices designed to reduce sleep disturbance associated with psychiatric conditions. Subsequent devices may go through FDA's 510(k) premarket process to gain marketing authorization by demonstrating substantial equivalence.

Nightware's study evaluated safety using validated measurements of suicidality and sleepiness, finding no changes in either measure for both the sham and active groups, FDA said. Sleep was assessed with two versions of the Pittsburgh Sleep Quality Index scale using a self-rated questionnaire to assess sleep quality, including a version intended for patients with PTSD.

FDA said the device is not a standalone therapy for PTSD and should be used in conjunction with prescribed medications and other recommended therapies.

Source: Medtech Dive

The global pandemic we’ve been dealing with since March has had a profound impact on our mental health. If this feels like the universe is piling on, it’s because it’s true. More than 264 million people worldwide suffer from depression, according to a January 2020 report from the World Health Organization.

Now, workers around the world say that 2020 has been the most stressful year in their lives, according to a new study by Workplace Intelligence, my research firm, and Oracle. Mental health isn’t a new issue, but COVID-19 has intensified and put a spotlight on our mental health problems.

Our study reveals some pretty stark facts:

• 70% of people have had more stress and anxiety at work this year than any other previous year.

• This increased stress and anxiety has negatively impacted the mental health of 78% of the global workforce, causing more stress (38%), a lack of work-life balance (35%), burnout (25%), depression from a lack of socialization (25%), and loneliness (14%).

• The new pressures presented by the global pandemic have been layered on top of everyday workplace stressors, including pressure to meet performance standards (42%), handling routine and tedious tasks (41%), and juggling unmanageable workloads (41%).

Work and life are blended

Because of the pandemic, a vast number of people are now working from home. Many of us have swapped offices and cubicles for living rooms and bedrooms. With no physical boundary between work and home, our personal lives have also been adversely affected.

An unhappy home life leads to even more depression, diminished productivity at work and yet more anxiety. It’s a vicious cycle.

Additionally, our daily commutes – what used to be a respite from the demands of both home and work – has been replaced by more work. The time we used to spend listening to music or podcasts or reading the paper has been filled with more Zoom meetings and late hours poring over spreadsheets.

Add to that a constant barrage of news (including political news during an election year in the US) about rising COVID-19 cases and death tolls, and the stress on our mental health becomes almost intolerable. Meanwhile, our society stigmatizes people who reveal their mental-health issues. Even someone as powerful as Dak Prescott was derided by some for talking about his anxieties.

Who wants to tell their boss they’re feeling burned out or spend less hours online than their colleagues?

Judgement-free technology

It’s thus no surprise that so many people (68%) said they would rather talk to a robot about their mental health issues than to their managers. For many of them (64%), robots and chatbots represent a judgement-free zone where they can seek information without exposing their weaknesses to bosses and colleagues.

A robot isn’t going to judge you, it’s not going to think about your past history – and it’s going to be available 24 hours a day. For example, if you’re feeling stressed or overwhelmed at 11pm at night, instead of worrying about how you’re going to talk to your manager about it the next day, you could instead “talk” to a robot or chatbot. From there, the technology can guide you to the best resources for support, such as providing tips for reducing stress, best practices for battling anxiety, or even referring you to a professional if needed.

In fact, technology like artificial intelligence (AI) and digital assistants are already helping to improve the mental health of 75% of the global workforce. Respondents said AI is providing them with the information they need to do their job more effectively (31%), automating tasks and decreasing workload to prevent burnout (27%), and reducing stress by helping to prioritize tasks (27%). Additionally, AI has helped the majority of workers shorten their work week (51%), take longer vacations (51%), increase productivity (63%), improve job satisfaction (54%), and improve overall well-being (52%).

Many companies are now offering virtual therapy sessions for their workforce, and licensing mental health apps their employees can access for free. Starbucks, for instance, offers their employees access to mental health support via self-guided online programs or video sessions with trained professionals. Starbucks also offers subscriptions to Headspace, the daily meditation and mindfulness app, to US and Canada partners.

A partnership between man and machine

This is important not just for employees, but for companies that want to attract and retain talent. Eighty-nine percent of employees would stay with an employer longer if they provided mental health support, and two-thirds wouldn't work for a company that didn't have a clear policy on supporting mental health, according to a November 2019 study by Aetna. A different study found that companies with mental-health programmes in place for one year had a median annual return of investment of $1.62 for every dollar invested.

This isn’t to say that technology will replace human beings. Robots and chatbots can’t empathize with us, and they can’t even diagnose our mental illnesses. We still need human therapists – today, perhaps more than ever – but technology can supplement the valuable work of human beings. Because even therapists can feel anxiety, and they could use a little help from our robot friends.

Source: World Economic Forum

Genetic sensors that can detect the activity from genes, rather than just the genes themselves, have been developed by a team led by University of Warwick scientists.

Based on the CRISPR gene editing system, the scientists from Warwick and Keele universities have developed microscopic machines that use these sensors to detect when genes are 'on' or 'off' inside a cell, and react to those changes dynamically—making them a potentially ideal monitoring system.

These genetic sensors are detailed in a new paper published in The CRISPR Journal, where the scientists demonstrate a genetic device based on the CRISPR system inside a bacterial cell. The work is the first step in scientists developing genetic devices that can make changes to gene expression after sensing the existing gene activity within a cell.

Lead author Professor Alfonso Jaramillo from the School of Life Sciences at the University of Warwick said, "Currently, we don't know how to design novel genetic systems to see which genes are on or off inside a cell. In nature, there are proteins that do that, they can sense the status of the cell, and the best we can do is to take those from one organism and put them in another one.

"We wanted to approach a new way of doing this, from scratch, to ask how we can program a system to listen to whatever we want inside a cell.

"Cells contain a number of genes that are expressed to perform numerous functions, from sensing their environment and processing food. By having a sensor that can detect when those genes are active, scientists could program a machine to react to a specific process, such as when the cell digests its food."

The researchers based their genetic device on the CRISPR system which is now broadly used for a variety of gene editing applications, including gene therapies. CRISPR molecules allow scientists to target and modify specific genomic sequences within cells. The advantage of the CRISPR system is its programmability, which allows it to be redirected to virtually any genetic targets, such as genetic mutations causing diseases.

To generate these novel genetic devices, the scientists used as a scaffold the programmable part of CRISPR which is also responsible for sequence recognition and binding, called guide RNA sequence (gRNA). They were able to redesign the gRNA sequence by introducing in it a sensor so that the CRISPR complex would be able to bind the DNA target only after being activated by a trigger signal, such as short segments of viral RNA sequences. The sensor can be triggered by any chosen RNA sequence and in this way it activates a CRISPR system at any point of the life cycle of a cell or virus.

The authors tested the genetic devices also in living Escherichia coli bacteria, by introducing a fluorescent gene that they could switch on or off only after interaction between the sensing device and the triggering molecule. They further validated their system to detect an RNA molecule deriving from the HIV virus, exemplifying its potential usability in medicine.

The scientists believe their system will be useful for many researchers looking to program cells with greater sophistication, for example to generate new synthetic circuits.

Dr. Jaramillo adds, "This is quite different from gene editing, where you simply modify the genome. This is about watching the behavior of the genome. If you have a monitor of the cell's behavior then you can make the cell correct that behavior if you don't like it, you can suppress it, or you can exploit that to switch on other genes.

"The drive is to have a genetic device able to monitor the behavior of a cell. Monitoring the behavior allows us to reprogram the cell to respond to specific signals, this is the first step towards so many other things."

Co-lead author Dr. Roberto Galizi, from the School of Life Sciences at Keele University said, "Coupling a genetic sensor with CRISPR tools offers an unprecedented opportunity for researchers to take genetic editing technologies to a completely new dimension. Eukaryotic cells could be programmed to detect deleterious mutations that may arise within its own genes, or to respond when invaded by pathogens like bacteria naturally do against phages.

"One interesting feature is that we can program these molecular tools to sense any predesigned RNA molecule in a sequence-specific manner and, at the same time, target any desirable gene or genetic sequence to stimulate various genetic actions, all within the same cell.

"Even genetic technologies aimed to control vector-borne diseases could benefit of such innovation. For example, we could engineer mosquitoes to sense and counteract pathogen transmission, or even mutations that makes vector or pest insects resistant to insecticides."

Source: PHYS

임산모 및 아이의 건강을 위한 제품과 서비스를 제공하는 오브맘(Ofmom)의 LIFE OFMOM APP이 이번 ‘2020 굿디자인 어워드(Good Design Award)’에 선정되었다.

우수디자인상품(GD) 상품선정은 산업통상자원부 주최 및 한국디자인진흥원 주관으로 산업디자인진흥법에 따라 1985년부터 매년 시행되고 있으며, 공정한 심사를 거쳐 디자인이 우수한 상품과 서비스에 정부인증마크인 GD(Good Design)마크를 부여하는 제도이다.

오브맘은 임산모와 아이의 건강, 영양에 대해 연구하고, 관련 제품과 서비스를 제공한다. 한국, 중국, 이탈리아에서 제공하는 프리미엄 산후조리원 '센트레 오브맘(Centre Ofmom)'을 기반으로 임신육아에 대한 방대한 경험과 지식을 축적해 왔으며, AAT Cattolica Universita (Italy), 이탈리아 제멜리 병원 및 중국 협화병원과 협업해 국제적 수준의 설비와 산업 네트워크를 구축했다.

이번 ‘2020 굿디자인 어워드’에 선정된 LIFE OFMOM APP은 사용자가 임신 전부터 육아 단계까지의 全단계적인 임신육아케어 서비스를 모바일을 통해 집에서도 편안하고 손쉬운 사용을 가능하게 해주었다.

LIFE OFMOM APP 서비스

LIFE OFMOM APP의 기능은 크게 세가지로 나눌 수 있다. 그 중 첫번째는 ‘임신육아 유저 맞춤형 인터페이스’로 사용자의 상태에 따라 세 가지 인터페이스(임신 전/중/후)로 분류하여 사용자 상태에 맞춘 다양한 서비스 지원이다. 두번째 기능은 ‘수유 가이드’로 사용자 상태와 아기 주수에 맞춘 수유 가이드 및 수유알림, 자가진단 테스트 및 실시간 보고서 서비스이다. 마지막 세번째로는 ‘당뇨, 비만 관리 및 1:1 전문가 상담’ 기능이다. LIFE OFMOM APP은 임신성 당뇨(GDM)를 겪고 있는 임산부들을 위해 당뇨병(1형/2형)을 구분하여 다양한 항목의 일일 기록 서비스를 제공하며, 이를 바탕으로 주기적 통계 그래프를 제공하여 사용자 스스로 관리할 수 있도록 도와준다. 또한 1:1 전문가 상담이 가능하여 음식, 임신성당뇨(GDM), 비만관리, 모유 수유 및 기타 육아 지식 서비스를 제공한다.

내 아이에게 더 좋은 것을 주고자 하는 어머니의 마음 Ofmom

사진(왼쪽부터): 한국, 중국, 이탈리아 센트레 오브맘, 쉐프 오브맘

오브맘은 영유아 토탈 케어 서비스를 책임지는 브랜드로, 한국, 중국, 이탈리아를 통해 최고급 산후조리원부터 액상분유, 영유아 용품 등 다양한 사업을 진행하고 있다. 엄마와 아이의 건강한 미래를 위해 전세계 각지 영유아 케어 병원 및 전문 의료진과 긴밀한 협업을 진행하여 제품 개발 및 최고급 서비스를 제공하고 있다. 오브맘은 2005년, 오브맘 시리즈를 포함한 액상 조제유 출시를 통해 세계를 향한 첫 번째 발걸음을 내딛는 계기가 되었다. 또한 한국 서울, 중국 북경, 그리고 이탈리아 로마에 위치한 프리미엄 산후조리원 ‘센트레 오브맘’은 단순히 산모와 아이에게 산후 조리 서비스 제공을 넘어 전 세계 산모와 아기의 누적된 데이터를 사용하여 이를 바탕으로 모자 보건 증진 연구에 활용, 개발되고 있다.

모자 보건 증진을 위한 Ofmom이 그리는 청사진

사진(왼쪽부터): 북경 협화병원, 이탈리아 제멜리 병원, 이탈리아 센트레 오브맘

50년 이상의 제약 기술을 보유한 오브맘은 한국 프리미엄 산후조리원 센트레 오브맘, 중국의 권위있는 의료기관 북경 협화병원, 이탈리아 AAT 카톨릭 대학 제멜리 병원과 합작하여 모자 보건 연구를 위한 연구개발을 지속해 오고 있다. 지난 수 년간 오브맘은 전세계 협업기관과의 합작을 통해 통합 치료 연구 기반으로 설계된 개인 검진을 통한 데이터를 수집하여 오브맘만의 글로벌 빅데이터를 구축하였다. 또한, 오브맘은 이탈리아 유제품 전문 기업인 그라나롤로(Granarolo)와 합작하여 포뮬러 액상 분유를 출시했다. 이는 오브맘과 세계적 기술력을 가진 이탈리아 유업조합이 만든 전세계 유일의 약품과 분유 기술 융합의 액상 분유이다.

오브맘은 자사만의 빅데이터를 적극 활용하여 단순히 질병 치료를 위한 서비스 제공을 넘어서, 빅데이터를 활용한 오브맘만의 인공지능(AI) 콘텐츠와 핵심 로직 기술을 구축하여 모자 보건 증진을 위한 목표를 이루어 나갈 예정이다.

전세계 유일한 포뮬러 – 글로벌 유명 대형 유업조합의 조화

사진 : 오브맘 유제분유

오브맘은 “엄마의 모유를 닮은”을 모티브로 이탈리아 고품질 원유 사용, Nutri-balance™의 배합과 최상의 영양 밸런스 및 영양소 파괴 없는 One-Direct Process, 그리고 3중 보호 포장으로 아이의 성장과 발육에 필수적인 영양소를 안전하고 건강하게 제공하고 있다.

모유는 면역성분과 미생물, 각종 풍부한 영양소들을 공급하여 신생아의 면역력을 높이고 균형 잡힌 영양소를 갖춘 아이의 중요 영양 자원이다. “엄마의 모유를 닮은” 오브맘은 모유수유가 어려운 산모들, 혹은 모유와 동시에 사용 가능한 제품으로 한국, 미국, 중국, 이탈리아 등 전세계 소비자들에게 큰 인기를 얻고 있다.

글로벌제약과 바이오네트워크를 기반으로 보건과학과 식품과학의 접목을 바탕으로, 임산모 및 아이의 건강을 위한 고품격 제품과 서비스 개발에 지속적인 투자를 해온 오브맘은, 이의 노하우와 빅데이터의 집대성인 세계 최초 유저 맞춤형 LIFE OFMOM APP의 굿디자인 어워드 수상으로 탁월한 제품력과 서비스, 그리고 디자인까지 글로벌 선두기업임을 보여주고 있다.

Artificial intelligence is lauded for its ability to solve problems humans cannot, thanks to novel computing architectures that process large amounts of complex data quickly. As a result, AI methods, such as machine learning, computer vision, and neural networks, are applied to some of the most difficult problems in science and society.

One tough problem is the diagnosis, surgical treatment, and monitoring of brain diseases. The range of AI technologies available for dealing with brain disease is growing fast, and exciting new methods are being applied to brain problems as computer scientists gain a deeper understanding of the capabilities of advanced algorithms.

In a paper published this week in APL Bioengineering, by AIP Publishing, Italian researchers conducted a systematic literature review to understand the state of the art in the use of AI for brain disease. Their search yielded 2,696 results, and they narrowed their focus to the top 154 most cited papers and took a closer look.

Their qualitative review sheds light on the most interesting corners of AI development. For example, a generative adversarial network was used to synthetically create an aged brain in order to see how disease advances over time.

"The use of artificial intelligence techniques is gradually bringing efficient theoretical solutions to a large number of real-world clinical problems related to the brain," author Alice Segato said. "Especially in recent years, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible to significantly increase the understanding of complex brain mechanisms."

The authors' analysis covers eight paradigms of brain care, examining AI methods used to process information about structure and connectivity characteristics of the brain and in assessing surgical candidacy, identifying problem areas, predicting disease trajectory, and for intraoperative assistance. Image data used to study brain disease, including 3D data, such as magnetic resonance imaging, diffusion tensor imaging, positron emission tomography, and computed tomography imaging, can be analyzed using computer vision AI techniques.

But the authors urge caution, noting the importance of "explainable algorithms" with paths to solutions that are clearly delineated, not a "black box" -- the term for AI that reaches an accurate solution but relies on inner workings that are little understood or invisible.

"If humans are to accept algorithmic prescriptions or diagnosis, they need to trust them," Segato said. "Researchers' efforts are leading to the creation of increasingly sophisticated and interpretable algorithms, which could favor a more intensive use of 'intelligent' technologies in practical clinical contexts."

Source: Science Daily

Bioprinting and bioprinters could help transform medicine, getting new organs and therapies to where they're needed faster.

You can print a lot of things with a 3D printer. A gun, a home, a dinner. Soon, you could even print new pieces of yourself.

While most uses of 3D printing involve extruding layers of plastic through a nozzle to create a three-dimensional structure, before too long, similar technology could let physicians print structures made of human cells -- from tiny structures like 'organs on a chip', to huge ones like whole replacement organs.

"Bioprinting has a great promise -- it has a lot of advantages and capabilities. Of course, it's not really perfect yet, but despite that, we have all these good things going on in the field," says Dr Ibrahim Ozbolat, principal investigator at Penn State University's bioprinting-focused Ozbolat Lab.

One of those things is making replacement organs. The process of bioprinting human tissue is a bit more involved than, say, 3D printing a new desk toy. First, you have to get some stem cells from the person who needs the new organ, culture them in the right biochemical soup until you've got enough, then turn them into a bioink that can be extruded through a nozzle that's two microns thick (or one 80th the size of a human hair). The bioink will be pushed through the printer, usually onto a scaffold made of hydrogel. A bit more culturing, and you could have a useable tissue that can either be printed directly onto the patient in an operating theatre, or built in a lab and then implanted.

Bioprinting isn't a fast process, but it could make a substantial impact in healthcare, not least in offering a solution to the chronic shortage of donor organs. In the US, for example, there are over 112,000 people still on transplant waiting lists.

Other than there simply aren't enough of them, another problem with donated organs is that recipients' immune systems can attack them, causing the donor organ to fail. If that doesn't happen in the first few days or weeks, it will eventually -- kidneys donated from living donors tend to last around 12 to 20 years. People with transplanted organs also need to take medication to suppress their immune system long-term. While those drugs lessen the chance of organ rejection, it also leaves those taking them at risk of other diseases such as certain cancers.

Bioprinted organs made from an individuals' own tissue won't be rejected by their body, will last far longer, won't need anti-rejection meds, and can be custom made to the individual's exact measurements -- whether they're a four year old or a NFL linebacker.

That's the goal, anyway. So far, most human organs that have been made in the lab and not got as far as being implanted into people. Not all human organs are created equally -- or can be created by bioprinting, for that matter. Flat tissues, like skin, and hollow ones, like the stomach or bladder, are relatively easy to print, whereas complex solid organs -- the heart, liver, or pancreas -- would be far harder to recreate with printing due to the rich blood supply they need.

The problem, according to Dr Anthony Atala, director of the Wake Forest Institute for Regenerative Medicine, is the blood vessels of larger bioprinted organs.

"Vascularity still remains a challenge because there's so many cells per centimetre [in large organs like the heart] that you really need a lot of vascular supply and nutrition. So to create the large structures is still a challenge, even though the printer is definitely helping in that area, but we're not yet ready."

The bigger an organ is, the more blood supply it needs to bring organs and nutrients to the tissue. Large organs need a complex web of interconnected, different-sized arteries, capillaries, and veins. The walls of the vessels need to be strong enough to withstand the normal flow of blood through them without causing clots, and need to be made of specific layers. For now, it's too much complexity for 3D bioprinters to manage.

While researchers are working on how to print full-size organs, the tiniest bioprinted structures are already helping researchers. Bioprinting can also be used to make 'organs on a chip' -- tiny samples of tissue that mimic the functions and structures of their full-grown counterparts. These mini organs allow pharmaceutical companies to test drugs on versions of human tissues, and assess their effectiveness or toxicity instead of using unreliable and ethically difficult animal models.

One day, organs on a chip could be made using individuals' own cells to test potential therapies. Rather than using the same standard treatments for every patient, by taking some cells, culturing them and printing them onto the chip, physicians can have a unique view into how their patient will react to a particular drug without having to start them on a whole course of it.

"These miniature human organs we can use for drug discovery, direct toxicity testing, and personalised medicine, BCS modelling and personalised medicine. We've taken the same strategy, and by using the same printers, we can print miniature structures that replicate the normal human response," Atala says.

As well as printing healthy tissue, bioprinters can make samples of diseased tissues -- like cancers -- to investigate current and future therapies.

"We're trying to find a way to create an effective treatment for solid tumours. I work with immunologists [who] engineer immune cells and make cytotoxic cells against the cancer cells. Now we're trying the immune cells in different 3D models that we print or we build. This could be used as a pre-screening tool for immunotherapy: so rather than directly going and checking things on the patient, this will be an intermediary step to screen the effectiveness of the therapy," Penn State's Ozbolat says.

And that's not the only way that bioprinting -- or rather, BioPrinters -- could help develop new therapies for common health conditions.

While HP's printers are more associated with offices rather than labs, HP also sells printers to the life sciences industry with its D300 BioPrinter line which prints drugs instead of documents. The machines are typically used in small to medium pharmaceutical companies in secondary drug discovery, where compounds thought to be effective against a particular disease are tested to see if they have any affect against the condition, and if so, at what dose.

The life sciences printer came into being, according to Annette Friskopp, global head and general manager of Specialty Printing Systems at HP Inc, after a group of HP engineers began researching the highest value fluid in the world that could be jetted through a thermal inkjet print head or similar. Was it perfume, jet fuel, rare chemical compounds?

"One of the highest value fluids that they discovered in this exploration was drug compounds… If you've ever printed a photograph on an HP printer, just think of how many small droplets of ink have to directionally be sprayed down onto that piece of paper, so when the photograph comes out of your printer, there's your friends and family. Leveraging exactly that same technology, that is [drug] dispensing using thermal inkjet technology," she says.

Viiv Healthcare's David Irlbeck has been using HP's D300 in his work to create latency reversal drugs for HIV. (Latency reversal involves changing an HIV infection from its latent phase to an active one, the body can mount an immune response against it, and is thought to be a promising avenue in the search for a cure for HIV.)

Viiv uses HP's D300 bioprinter for measuring out very small, very accurate amounts of particular compounds to see how their effectiveness changes at different doses. The machine is, says Irlbeck, "very user friendly, very easy to program, and it can do titrations that would be extremely difficult to do manually". By having a printer print out the drugs, researchers are able to get them down to finer and finer concentrations that human researchers simply wouldn't be able to make by doing the same process by hand, and would likely be less accurate if they did. And, because printers can measure out doses of drugs at tiny and highly accurate concentrations, it allows pharmaceutical researchers to reduce any wastage of the (very expensive) compounds.

Bioprinters also enable researchers to combine two drugs in very precise concentrations to see if they might have a synergistic effect -- where the two drugs together might have a greater effect than that which both produce individually. "It's very, very technically demanding to do synergy work without an instrument like the D300," Irlbeck adds.

As well as working on HIV medications, the D300 was recently co-opted into working on treatment for SARS-CoV-2, the virus that causes COVID-19, prepping drug plates for a lab at the University of North Carolina, which was looking into their antiviral potential.

The bioprinters are also being used elsewhere in the fight against coronavirus. HP has donated four of the D300e BioPrinters to four research facilities working on COVID-19: the Spanish National Research Council, the Monoclonal Antibody Discovery Laboratory at Fondazione Toscana Life Sciences in Italy, the US Center for Nuclear Receptors and Cell Signaling (CNRCS) at University of Houston, and France's Grenoble Alpes University Hospital. Between them, the facilities are using the machines for research into the fundamental biology of COVID-19, monoclonal antibodies and other potential therapeutic candidates, and work on immunisation: the future of healthcare is likely to be 3D printed.

Source: ZDNet

The COVID-19 pandemic has proven the case for telemedicine, but if we are to see a proactive healthcare revolution then we must put remote testing firmly in the picture.

The pandemic has irreversibly transformed society as we know it - breaking down the very fabric of the way we live, work and engage with each other, catapulting us into a deep and potentially long lasting economic recession, disrupting education and dramatically changing our perceptions of healthcare and how it should be delivered.

Necessity has forced our hand, and we have seen a seismic shift in how we will view healthcare for generations to come. The events of 2020 have accelerated a macro political shift towards remote models of health and healthcare monitoring.

In the worst of the pandemic here in the UK, the pressing need to protect both healthcare providers and at-risk patients saw a dramatic rise in the use of telemedicine

Data from the Royal College of General Practitioners shows that 71% of routine consultations were remote in the four weeks leading up to April 12th, compared to 25% in the same period last year.

Throughout the most critical days of the pandemic, this model has ensured vulnerable people feel safe, and reduced footfall in GP surgeries and hospitals.

However virtual consultations, through video and phone, are only one part of the picture. Many consultations require testing as part of the follow up, to gain insights into their health and yet we’re seeing huge backlogs around access to blood testing.

The great opportunity is for an extension of telemedicine to include remote monitoring through at-home testing at scale, and as we move towards remote models of care, we must ensure that this critical element doesn’t get left out in the cold.

Through effective at-home blood testing, we will start to see real impact on health outcomes, with the additional benefits of lower costs and reduced pressure on GP surgeries and hospitals.

At Thriva, we are already working with hospitals and healthcare providers including the Royal Brompton Hospital and Kings College Hospital.

The potential is great. To take just one example area, there are 3.9 million people living with diabetes in the UK - roughly they each need a blood test every three months. You can add to that the millions of people who are on powerful drug treatments and require blood tests to monitor their liver function, or those who require regular check-ups whilst they’re on cancer treatments.

Many of these tests can be delivered remotely, at home using a finger-prick blood sample that patients can undertake themselves or by capitalising on the network of nurses who can drop into patients’ homes. Many tests can be delivered by post, with results accessed online by both medical practitioners and the patient, increasing patient engagement, reducing inconvenience for patient and increasing the likelihood of adherence. In short, at-home testing, if done right, is actually what patients are looking for in many situations.

Of course, it won’t be suitable for everyone - some populations will struggle to take a test themselves, and the long-held criticism of remote care is that it has the potential to leave the most vulnerable patients behind.

We need to recognise these limitations and seek to work closely with those who are responsible for the overall care to ensure that it meets the needs of all patients. But the opportunity far outweighs the hurdles.

If continued, this urgent shift (which this year we saw happen in days and weeks rather than months) has the potential to not only prioritise patient safety, but improve overall wellbeing and experience, with a customer-centric approach which enables people to take a proactive role in their own health.

This evolution towards remote testing could be transformative for the patient experience, accelerating a proactive, person-centric model vital to an effective and successful healthcare system of the future. In short, it has the potential to unlock a proactive healthcare revolution.

Source: Medtech News

Technology has improved industries across the world, from manufacturing to marketing. Though the healthcare industry has struggled more than most to adapt to the digital world, it’s finally coming on in leaps and bounds.

Healthcare organisations across the world are now using technological advancements their advantage, and some are very surprising.

Data analytics

Data analytics have been around for a while, but it’s only recently that the healthcare industry has started using them to their full potential. Using technology which can process big data, medical organisations have been able to study things like clinical trials, drug development, population, diet, etc.

With the information that data analytics provide, professionals can draw helpful conclusions on patterns, correlations or where their operations could improve. This is all invaluable when it comes to improving patient care.

Artificial intelligence

Artificial intelligence is helping healthcare organisations in a whole manner of ways. For starters, AI is being used to decipher the human genome, granting medical professionals a greater understanding of genetics.

It’s also being applied to bionic limbs, so people who have lost an appendage can be fitted with a prosthetic that responds to their brain signals. AI is helping to automate certain medical processes, too, removing the need for manual labour on redundant tasks.

Looking to the future, artificial intelligence is going to become increasingly vital in global healthcare. As such, venture philanthropists like Tej Kohli predict that the AI industry is going to be worth $150 trillion in five years.

Virtual and augmented reality

Virtual reality and augmented reality (AR) are two burgeoning forms of technology which are looking to revolutionise the healthcare industry. Virtual reality is when someone is immersed in a simulation with high-quality graphics that replicate the real world.

Scientists are looking to apply VR to medical training, reducing the costs and risks that would normally be involved. For instance, trainee surgeons could be entered into a hyper-realistic simulation of a surgery.

Augmented reality, on the other hand, is where computer-generated visuals are imposed on the real-world environment. Doctors are hoping to use AR to superimpose models of a patient’s anatomy onto the patient, improving their surgical accuracy.

Health trackers

Health trackers have become incredibly popular in recent years and now the medical industry is looking to use them for their own purposes. For example, a reliable wearable device could make it easier for doctors conducting clinical trials to monitor their patient’s vitals.

As a result, patients involved in the trials wouldn’t have to visit the hospital every day, which is normally a massive downside of participating. Health trackers are also incredibly useful for tracking diet and fitness levels, so doctors might prescribe them to their patients.

Source: Fraserburgh Herald

Scientists have long known that human genes spring into action through instructions delivered by the precise order of our DNA, directed by the four different types of individual links, or "bases," coded A, C, G and T.

Nearly 25% of our genes are widely known to be transcribed by sequences that resemble TATAAA, which is called the "TATA box." How the other three-quarters are turned on, or promoted, has remained a mystery due to the enormous number of DNA base sequence possibilities, which has kept the activation information shrouded.

Now, with the help of artificial intelligence, researchers at the University of California San Diego have identified a DNA activation code that's used at least as frequently as the TATA box in humans. Their discovery, which they termed the downstream core promoter region (DPR), could eventually be used to control gene activation in biotechnology and biomedical applications. The details are described September 9 in the journal Nature.

"The identification of the DPR reveals a key step in the activation of about a quarter to a third of our genes," said James T. Kadonaga, a distinguished professor in UC San Diego's Division of Biological Sciences and the paper's senior author. "The DPR has been an enigma—it's been controversial whether or not it even exists in humans. Fortunately, we've been able to solve this puzzle by using machine learning."

In 1996, Kadonaga and his colleagues working in fruit flies identified a novel gene activation sequence, termed the DPE (which corresponds to a portion of the DPR), that enables genes to be turned on in the absence of the TATA box. Then, in 1997, they found a single DPE-like sequence in humans. However, since that time, deciphering the details and prevalence of the human DPE has been elusive. Most strikingly, there have been only two or three active DPE-like sequences found in the tens of thousands of human genes. To crack this case after more than 20 years, Kadonaga worked with lead author and post-doctoral scholar Long Vo ngoc, Cassidy Yunjing Huang, Jack Cassidy, a retired computer scientist who helped the team leverage the powerful tools of artificial intelligence, and Claudia Medrano.

In what Kadonaga describes as "fairly serious computation" brought to bear in a biological problem, the researchers made a pool of 500,000 random versions of DNA sequences and evaluated the DPR activity of each. From there, 200,000 versions were used to create a machine learning model that could accurately predict DPR activity in human DNA.

The results, as Kadonaga describes them, were "absurdly good." So good, in fact, that they created a similar machine learning model as a new way to identify TATA box sequences. They evaluated the new models with thousands of test cases in which the TATA box and DPR results were already known and found that the predictive ability was "incredible," according to Kadonaga.

These results clearly revealed the existence of the DPR motif in human genes. Moreover, the frequency of occurrence of the DPR appears to be comparable to that of the TATA box. In addition, they observed an intriguing duality between the DPR and TATA. Genes that are activated with TATA box sequences lack DPR sequences, and vice versa.

Kadonaga says finding the six bases in the TATA box sequence was straightforward. At 19 bases, cracking the code for DPR was much more challenging.

"The DPR could not be found because it has no clearly apparent sequence pattern," said Kadonaga. "There is hidden information that is encrypted in the DNA sequence that makes it an active DPR element. The machine learning model can decipher that code, but we humans cannot."

Going forward, the further use of artificial intelligence for analyzing DNA sequence patterns should increase researchers' ability to understand as well as to control gene activation in human cells. This knowledge will likely be useful in biotechnology and in the biomedical sciences, said Kadonaga.

"In the same manner that machine learning enabled us to identify the DPR, it is likely that related artificial intelligence approaches will be useful for studying other important DNA sequence motifs," said Kadonaga. "A lot of things that are unexplained could now be explainable."

Source: PHYS

CSIRO researchers crunched one trillion genomic data points in the cloud to help locate parts of the human genome that cause disease.

The CSIRO's bioinformatics group used its own VariantSpark artificial intelligence (AI) based platform, which runs on Amazon Web Services (AWS).

In a new study published in the technical journal Giga Science, the researchers outlined how they analysed a synthetic dataset of 100,000 individuals’ genomes, each made up of over three billion DNA base pairs.

Dr Denis Bauer, head of the bioinformatics group, said no other technology platform has yet been able process one trillion data points of genomic data, over 10 million variants and 100,000 samples at once.

Using AI platforms in this way will be essential for the future of healthcare in Australia, CSIRO’s Australian e-Health research centre chief executive Dr David Hansen added.

"Artificial intelligence is a critical component of understanding genomic information," Hansen said.

"Despite recent technology breakthroughs with whole genome sequencing studies, the molecular and genetic origins of complex diseases are still poorly understood which makes prediction, application of appropriate preventive measures and personalised treatment difficult."

This is because many traits and disease are thought to be polygenic, or influenced by more than one gene, the Giga Science paper states.

VariantSpark was found to better identify genomic variants associated with complex genetic expressions compared to traditional monogenic, genome-wide association studies.

"Our research shows VariantSpark is the only method able to scale to ultra-high dimensional genomic data in a manageable time," Bauer said.

"It was able to process this information in 15 hours while it would take the fastest competitors likely more than 100,000 years to process such a volume of data.

"This is a significant milestone, as it means VariantSpark can be scaled up to analyse population-level datasets and drive better healthcare outcomes."

The paper concluded that VariantSpark is not a replacement for traditional genetic association analysis, but rather a complement.

“The results of traditional GWAS [genome-wide association studies] and VariantSpark should be considered together to gain insights into the full influence of the genome on disease and other phenotypes,” the authors wrote.

Source: itnews

Healthcare is perhaps the most important sector in the U.S. economy. It is the largest: close to $4 trillion per year is spent on healthcare in the United States. It employs more people than any other industry, accounting for 11% of all American jobs. Nearly one quarter of all U.S. government spending is on healthcare.

At the same time, healthcare is the most broken sector in the U.S. economy. Healthcare costs have spiraled out of control in recent decades, from $355 per person in 1970 to $11,172 per person in 2018. Despite spending far more on healthcare per capita than any other country, the United States ranks 38th in the world in life expectancy, between Lebanon and Cuba. Access to healthcare remains worse in the U.S. than in any other developed country.

Artificial intelligence offers an unprecedented opportunity to cut this Gordian Knot and reshape the practice of healthcare. Of the many ways in which AI will transform our lives in the coming years, its impact may be more profound and far-reaching in healthcare than in any other field.

Machine learning and healthcare are in many respects uniquely well-suited for one another. At its core, much of healthcare is pattern recognition. A healthy human body and its various subsystems function in consistent, quantifiable ways. When a human organism is suffering from some affliction, it deviates from this homeostasis in ways that tend to be predictable across time and populations.

A constellation of data points—recent physical symptoms, blood pressure, genetic makeup, the bloodstream’s chemical composition, and so on—can be collected that, taken together and compared against population-level patterns, tells the definitive story of a person’s health. Similarly, the medicines that we create and prescribe consist of specifically defined substances that act upon the body’s internal systems in measurable ways.

If there is one activity at which machine learning excels, it is identifying patterns and extracting insights about complex systems given lots of data. Healthcare therefore represents an ideal challenge for AI.

In which specific areas of healthcare can we expect AI to have a significant impact? It is helpful to break down the sprawling field of healthcare into three main categories: clinical (the delivery of care to patients), administrative (the operational nuts and bolts that keep the healthcare system running), and pharma (the research and development of new medical drugs).

In each of these three areas, machine learning is already being applied in transformative ways. This will only accelerate in the years ahead.

This article will review the first two of these categories: Clinical and Administrative. The third category, Pharma, will be covered in a followup article.

Clinical

- Imaging

Using computer vision to identify health conditions in medical images has become perhaps the most widely referenced use case for AI in healthcare. It is easy to understand why: examining a medical scan to determine whether a tumor, a skin lesion, a retinal disease, or some other indication is present is a clear-cut example of object classification, exactly what deep learning excels at.

As AI legend Geoff Hinton famously declared in 2016, “People should stop training radiologists now. It’s just completely obvious that within 5 years, deep learning is going to do better than radiologists.”

A number of startups have emerged over the past few years to automate analysis of medical images. Among the more notable are Caption Health, PathAI, Paige, and Zebra Medical Vision.

Yet despite the hundreds of millions of dollars of venture capital that has flowed into this category, the technology has not yet been widely adopted. It has proven challenging for AI companies to convince healthcare providers to alter their workflows to incorporate these solutions at scale—particularly given that this use case so directly threatens to render human practitioners obsolete.

- Patient Intake and Engagement

Another area in which AI will improve care delivery is patient intake and engagement, a critical part of the healthcare journey.

Recent advances in natural language processing have made possible AI-based conversational interfaces that can automate patient screening and care navigation. For example, patients can share symptoms and questions via text message and receive automated clinical guidance in response. Similarly, AIs can be developed that communicate with patients on an ongoing basis to ensure that they remain engaged and compliant with their care regimen.

Using AI to automate these interactions can dramatically reduce costs and democratize access to healthcare by making expert health guidance available without the need for human physicians’ expensive time.

“Chatbots” have generated plenty of criticism and unfulfilled hype in recent years. But NLP technology is now advancing at a breathtaking rate, opening up new possibilities for conversational AI. Conversational platforms are most effective when they are purpose-built for a specific use case (e.g., patient engagement) and are designed to loop in a human when appropriate (e.g., a doctor). Expect asynchronous provider/patient communication to become increasingly automated in the years ahead.

The behemoth in this category is Babylon Health, which has raised an eye-popping $635 million, much of it from Saudi Arabia's Public Investment Fund. Other startups building tools to automate patient intake and communication include Buoy, Gyant, Curai, and Memora.

“The entire computing revolution has unfolded without fundamentally changing how we deliver and access healthcare,” said Curai CEO Neal Khosla. “AI and NLP offer the potential to massively scale the availability of quality primary care, making it accessible to more people at lower cost. That’s our north star: a world where all 8 billion people in the world can access best-in-class primary healthcare.”

- Remote Health

COVID-19 has greatly accelerated the adoption of remote health: the delivery of clinical services to patients over distance rather than in-person, using digital tools.

While the pandemic has served as a near-term catalyst, many experts believe that remote health (also called telehealth) is on its way to becoming a permanently important pillar of healthcare delivery. McKinsey estimates that up to $250 billion of healthcare spend will be virtualized in the coming years in the United States alone.

Today, telehealth often simply means a videochat with a clinician. Such remote sessions are worthwhile but rudimentary. Remote health will reach its full potential only when empowered by machine learning (and the right sensors). Several promising companies are tackling this challenge.

Eko has built a platform of proprietary sensors and machine learning algorithms that can remotely monitor patients’ cardiopulmonary vital signs for early detection of heart and lung problems. Eko’s AI is significantly more accurate at detecting heart problems than are human physicians using a stethoscope. For instance, general practitioners detect atrial fibrillation with 70-80% accuracy, while Eko's algorithms do so with 99% accuracy.

“We are able to augment the doctor’s judgment about cardiac and pulmonary diagnoses with data analyzed from tens of thousands of past patient exams in seconds,” said Eko CEO Connor Landgraf. “These algorithms can be accessed anywhere in the world, enabling better care for patients regardless of where they are.”

Along similar lines, Aluna offers a solution that enables patients to measure their lung health from the comfort of their home using a simple spirometer. Applying machine learning to the spirometry data, Aluna monitors asthma and cystic fibrosis in real-time and flags worrisome lung conditions.

Companies like Biofourmis, Current Health, and Myia have likewise developed AI and sensor solutions that enable practitioners to examine a patient's health at a granular level, wherever that patient is in the world. Technologies like these will increasingly blur the line between “in-clinic” examinations and day-to-day health monitoring—making healthcare more affordable and accessible in the process.

- In-Hospital Care

As promising as telehealth is, there will always be medical procedures that necessitate in-person visits. AI will augment the work of human clinicians in hospitals in various ways.

As one example, Gauss Surgical uses computer vision to monitor blood loss during childbirth. Human clinicians’ visual estimation of blood loss is notoriously inaccurate, and hemorrhage is the leading preventable cause of maternal mortality. At one hospital system, Gauss’ AI solution led to a 4x increase in hemmorhage recognition and a 34% reduction in delayed bleeding interventions.

As another example, Medical Informatics is a Houston-based company that uses machine learning to monitor the well-being of patients in hospital beds by ingesting and synthesizing data from bedside monitors, ventilators, a patient’s EMR, and various other data sources.

Even if AI solutions like these never replace human clinical decision-making and serve only as supplemental tools, they offer the potential to dramatically improve health outcomes and save lives.

- Precision Medicine

In a way, precision medicine represents the pinnacle of AI’s promise to improve human health. The vision of precision medicine is more ambitious, the technical challenges more complex, and the potential impact greater than perhaps any other application discussed here.

In a nutshell, the field of precision medicine aspires to create treatments that are individualized for each patient based on his or her particular genetic, environmental and behavioral context.

Precision medicine is not a new concept, but the advent of “big data” (especially genetic data) and modern machine learning have brought its full realization within reach. Due to the proliferation of sensors, internet-connected devices, EHRs, mass-market gene sequencing, cloud computing, and other digital technologies, staggering amounts of highly detailed health data are now collected every day. Several trillion gigabytes of health data will be generated this year, a figure that would have been unimaginable a few short years ago.

The premise of precision medicine is that if a computational system knows your entire genome, your metabolic profile, your microbiome composition, what foods you eat, how often you exercise, how much you sleep, and a thousand other data points about you; and it also understands a disease’s particular pathway in your body down to the molecular level; then it can synthesize all this information and craft a pharmaceutical and/or behavioral regimen specifically tailored to optimize your body's response.

No human could ever perform such herculean data crunching and latent pattern recognition. For the first time, AI makes it possible—at least in theory.

The most prominent company pursuing this lofty vision of AI-powered precision medicine is Tempus. Tempus has raised a whopping $620 million from investors including NEA and T. Rowe Price. The company is focused on cancer treatment, although it has recently devoted resources to the fight against COVID-19.

Other well-funded companies in this category include Syapse and GNS Healthcare.

Precision medicine has for decades stood as a tantalizing but unfulfilled possibility. Time will tell whether AI is the key that can unlock its vast potential.

Administrative

Compared to clinical or life sciences use cases, applying AI to the administrative side of healthcare may seem unglamorous. But an enormous opportunity for value creation exists here.

As anyone who has dealt with the healthcare system knows, it is plagued by waste and inefficiency. Over $600 billion per year is spent on healthcare administration and billing in the U.S. alone. Many billions of dollars of value will be unlocked in the years ahead by rationalizing and streamlining healthcare operations. AI can play a key role here.

- Provider Operations

Every time a patient interacts with a healthcare provider, dozens of support processes take place in the background: patient check-in, benefit and verification discovery, claims processing, invoicing, prescription orders, supply chain management, and more. The way this work gets done today is manual and error-prone.

A promising set of companies is applying machine learning to automate many of these rote tasks. Perhaps the buzziest is Olive, which has raised $125 million from investors including General Catalyst and Khosla Ventures. Notable Health is a newer competitor with a similar mission. Leveraging software bots and computer vision, these companies’ solutions can be thought of as robotic process automation (RPA) built specifically for healthcare use cases.

One administrative function that is especially challenging and important for healthcare providers is revenue cycle management (RCM). RCM refers to the set of processes that providers use to track and collect payments for services rendered to patients.

Because of the U.S.’ third-party payer structure and labyrinthine reimbursement system, money flows in the healthcare system are complex. Numerous stakeholders are involved: providers, patients, private insurers, government agencies, employers. Clerical errors and costly delays are rampant. An estimated $21.3 billion was spent on RCM in 2017 in the U.S. alone.

There is a massive opportunity for AI to systematize and automate revenue cycle management, making it faster, cheaper, and more accurate. One promising startup focused on RCM is Alpha Health.

Another major administrative challenge for providers is hospital patient flows and resource allocation. Hospitals are complex systems, with patients and clinicians constantly moving through their units in dynamic ways. Hospitals’ razor-thin margins depend on orchestrating these flows efficiently. Yet at present, they are dramatically underoptimized. Up to 25% of ICU patient-days are unnecessary; an estimated 15% of total hospital occupancy is wasted due to ineffective flow management.

This is exactly the type of data-rich optimization problem at which AI excels.

Qventus is one company applying AI to drive better operational outcomes for hospitals. The company claims its technology has enabled hospitals to achieve a 30% reduction in excess days spent in hospital, a 20% decrease in ER door-to-doctor times, and a 0.8 day reduction in average length-of-stay. These operational efficiencies translate to better patient experiences and significant improvements to health systems’ bottom lines.

Finally, AI can unlock massive gains by automating regulatory compliance. Healthcare is, for good reason, one of the most heavily regulated of all industries. It is challenging and expensive for healthcare providers to keep track of and ensure adherence to the many legislative and regulatory requirements to which they are bound.

Two important areas are patient data privacy and controlled substances management. In both cases, machine learning can play a key role automating compliance activities like violation detection and auditing—thus protecting patients, bringing down costs, and allowing clinicians to focus their energy on care delivery. One company worth watching in this category is Protenus.

- Data Infrastructure

One of the foundational challenges standing in the way of a better healthcare system is its deeply fragmented data landscape. Stringent regulatory restrictions, archaic software architectures, and misaligned stakeholder incentives all undermine valuable data sharing and collaboration. It is prohibitively difficult today to assemble a complete picture of a single patient’s health, a new treatment’s efficacy, or a population’s health patterns.

Data silos in healthcare are not just a bureaucratic burden. They hold back progress in medical research and impede delivery of the right care to the right patients at the right time, ultimately costing lives.

This is a sprawling, multi-dimensional challenge. A number of interesting companies are tackling different pieces of it: Komodo Health, Datavant, Abacus Insights, HealthVerity, Kyruus, Ribbon Health, and Redox, among others. These companies have differing product and go-to-market focuses but share the overarching vision of breaking down data barriers in order to drive better health outcomes.

Because machine learning thrives on large datasets, these solutions are also laying the groundwork for boundless future AI innovation. A more integrated data ecosystem will make possible countless new AI applications in healthcare, most of which have not yet even been imagined.

- Medical Documentation

One last administrative area in which AI is poised to generate massive value is medical documentation. Recording notes from patient encounters consumes a significant portion of clinicians’ working lives. In the era of electronic health records (EHRs), this has become a real problem.

A recent AMA study found that the average on-duty physician spends 5.9 hours per day directly engaged with EHRs. There is widespread concern in the medical profession that, with computers in every examination room today, doctors must be so oriented toward their keyboards during patient visits that they are not able to connect fully with patients.

Machine learning can take over much of this administrative burden from physicians, allowing them to spend more time with patients and less time with screens.

The core technologies underlying natural language processing and speech recognition have improved dramatically over the past several years, as anyone who uses Alexa or Siri can attest. This has enabled the development of voice-based solutions (“AI scribes”) that physicians can verbally dictate to in lieu of manual EHR data entry. These solutions can be built to automatically integrate with existing workflows and with EHR software like Epic or Cerner.

The efficiency gains can be tremendous. Augmedix claims that its voice-based solution saves clinicians 2 to 3 hours per day. Suki, another competitor in this category, says that its AI generates 100% accurate notes and enables doctors to finish their notes 76% faster. Scaled across entire health systems, the cumulative impact of these technologies will be enormous.

Today, these solutions still require humans in the loop for quality control; NLP and speech technologies, while impressive, remain imperfect. As the underlying AI continues to improve, less and less human intermediation will be required—translating to even greater productivity gains and cost savings.

Conclusion

Healthcare is an intimate part of our personal and family lives in a way that no other sector of the economy is. It is therefore particularly troubling how dysfunctional the healthcare system is today.

No technology can be a silver bullet for a system as complex as modern healthcare. Yet artificial intelligence, perhaps more than any other force in the world, offers the potential to rewrite the rules of the game. When deployed thoughtfully, AI can upend long-accepted constraints and assumptions about how the healthcare system works. It can redefine the relationship between cost, accessibility and quality—one that today is badly broken.

There has never been a more exciting time to be an entrepreneur in healthcare.

Source: Forbes

In 2015, South Korea was taken by surprise with an outbreak of the Middle East Respiratory Syndrome-related coronavirus. At the time, it was deemed the largest outbreak outside of the Middle East, totaling 186 cases and 38 deaths. Since then, the Seoul government has grown more vigilant and has reformed its health system to handle outbreaks, using digital health technology that works seamlessly between different strategic institutions. Experts believe it is this shift to digital preparedness that has allowed South Korea to successfully manage the ongoing coronavirus disease (COVID-19) pandemic. Although it was one of the first countries to detect COVID-19 cases, it has managed to keep infection and death rates low without shutting down businesses or enforcing widespread lockdowns.

At the onset of the pandemic, the government swiftly and decisively activated a holistic system of surveillance, case identification, contact tracing, and quarantine measures using the best of digital health technologies. In a report published by the South Korean government in April, titled “Flattening the curve on COVID-19: How Korea responded to a pandemic using ICT,” we learn about a number of these innovative initiatives that have reduced the menacing impacts of the virus in the country. One example includes a university student who developed a “Coronavirus Map” app that alerts users on patients’ movements in their vicinity, fetching data from the Korea Centers for Disease Control and Prevention and essentially providing a map of infection hot spots. Another app, Cobaek, notifies users whenever they are within 100 meters of a confirmed coronavirus patient.

The government has also imposed a 14-day quarantine rule for all inbound local and foreign travelers. To monitor any breaches, the government has created the “Self-quarantine Safety Protection App,” which must be downloaded by all arrivals. This app enables users to monitor their health condition using a checklist of possible coronavirus symptoms. Authorities are also immediately notified if someone attempts to venture outside their designated quarantine area. Furthermore, epidemiological investigators developed a digital system that allows them to immediately identify the transmission routes of coronavirus patients using data from telecommunications companies, credit card companies, and the police. Such real-time data enables the government to alert local citizens about emerging hot spots, test or quarantine people who have been in contact with confirmed patients, and manage health care services in infected areas.

Today, governments are navigating unknown terrain, with every decision being a double-edged sword of trying to save people’s lives while potentially destroying their livelihoods. However, digital health technologies are proving to be an essential navigational compass in the battle against COVID-19. During this pandemic, for example, governments have harnessed the powers of digital technology to respond in a reliable, timely and effective manner. Countries are using it to review real-time data on transmission and death rates according to demographics and geographical locations, survey population movements using GPS on mobile phones, manage health care resources and medical equipment, communicate with the public, enforce quarantine measures, and evaluate the effectiveness of intervention strategies.

For example, online health dashboards, such as those run by the World Health Organization and the Johns Hopkins Coronavirus Resource Center, are helping policymakers, health care professionals and citizens monitor how the virus is spreading. This detailed data set can then be garnered to identify emerging clusters of infections and to prepare health care systems accordingly.

Like South Korea, Singapore has also kept its COVID-19 infection and death rates low by using an array of digital tools. “Ask Jamie” is a virtual assistant that can answer citizens’ queries on the virus and can be accessed across 70 government agency websites. It has also been enhanced to address business queries, including providing information on how the government is supporting businesses during this crisis. Citizens may also subscribe to the official government WhatsApp account to receive timely and credible updates on the COVID-19 situation in four languages.

In Singapore, people have their temperature checked at high-traffic entry points such as schools, workplaces and on public transport. “VigilantGantry” has been developed as a fully automated, contactless temperature screening tool that can detect abnormal temperatures without the need for staff. The data from the system and facial recognition technology is used by the authorities for contact tracing and to detect emerging hot spots of the disease. Additionally, the government has developed the “TraceTogether” app, which exchanges Bluetooth signals when people are in close proximity to one another. These records are stored for 21 days in each user’s phone. Should an individual be diagnosed with COVID-19, the Ministry of Health can easily access this data to identify those who were in close contact with the patient and test or quarantine them accordingly.

Taiwan has also managed to successfully control the pandemic using digital health technologies. With a population of 23 million, it has kept COVID-19 cases to fewer than 500 and recorded just seven deaths. When China first reported the outbreak, Taiwan swiftly imposed health checks for incoming travelers from Wuhan, bringing together data from immigration checkpoints with its health insurance system. This allowed health care workers to access patients’ travel histories and initiate testing, contact tracing or quarantining as needed. Furthermore, home-quarantined individuals were monitored electronically by government-issued mobile phones and, in the case of breaches, authorities were alerted and individuals issued fines.

Digital health technologies have played a crucial part in quelling the pandemic in countries with low infection and death rates. It seems, then, that we can help steer the course of this pandemic by capitalizing on these digital solutions.

Source: Arab News

The method of medical diagnosis is incredibly complicated. There are plenty of known diseases, but only very few potential symptoms. Diagnosis is time-consuming and often requires multiple laboratory tests. There is plenty of opportunity for unintended mistakes and the human eye and intellectual ability can only do so much when it gets down to disease detection. Medical machine vision applications are changing this in less time, by offering real-time information to health care professionals this drive improved results in radiology, neuroscience, biological sciences and more.

Artificial Intelligence is altering the medical system to allow data analytics to be used by clinicians to diagnose and treat diseases. Doctors can diagnose problems more easily using image recognition, and epidemiologists can gain a greater knowledge of contagious diseases such as COVID-19. Medical AI investment is massive, with the segment-leading startups and large firms have attracted billions in investment capital in 2020 alone.

All of this begins with proper annotation of the picture. Effective models of artificial intelligence rely on accurate data from learning. The medical records, including CT and MRI scans, can be used to train the model of machine learning. They are indeed the fuel resources for the development of specific diagnostic and treatment methods. But to recognize the characteristics in that data, the computer needs to be trained or the human body is unconvincing simple.

AI must be educated with thousands of annotated image processing, each with suitably marked points or devices, for a medical AI system to operate. For instance, annotation can mark tumors, fractures or an indication for a communicable disease. Other systems may predict significant changes by evaluating a series of images captured over a period.

Recently, Covid-19 Researchers have professionally operated a team working with V7 Labs to annotate X-rays for the chest used it to educate and evaluate machine-learning prototypes that could help speed up COVID-19 screening. Indeed, biomedical AI lets hospitals get a good grip on the disease outbreak.

Enhance better treatment outcomes with AI. Here are 4 reasons in which medical AI can help to improve the quality of care when you outsource image annotation.

Shortened diagnosis and treatment timelines

Medical AI can easily detect visual evidence of medical symptoms, such as CT and MRI scanning, that it is being educated to identify, minimizing the time required to diagnose disease.

Speed is one of the most important benefits that AI provides. AI can process sensory information in a mere fraction of time it requires for a human to do the same. The sensors are at the best product that any possible way it is designed or produced with the rapidness. The image conversion process is also in par with the speed of the sensors.

Reduce the possibility of human error

Humans are heavenly people, and perhaps the finest of us would be prone to making mistakes. Thankfully, by automating daily workloads, most of those challenges are being avoided.

For the appropriate data sources, AI will help minimize the human error issue which is a contributing death cause. A well-trained model for machine learning will detect things that humans could not. This also makes for quicker and more educated decision-making to achieve better results. You might think of AI as the second-best thought you will ever have.

Offering healthcare precision

Clinical AI will have more customized and preventive information. The well-trained technology of medical AI uses the right information to analyze real-time decisions and to build forecasting analytics that can identify issues before physicians can to allow physicians make more informed decisions customized to each patient’s specific conditions.

Empowering rapid medical research-AI is used to evaluate and analyze trends in large datasets in medical science. This can collate across large databases of scientific literature and photographs, as one, and implement this abundance of preceding information to help anticipate potential in drug development ASAP.

This provides a great opportunity, provided that the expense of producing a new drug cost even the average cost. In addition, a new product usually takes years to reach the market, and the majority of such a time has been spent on clinical trials. Medical AI provides the ability to drastically reduce these time periods by evaluating test-related data to help plan more efficient and quicker tests.

Money invested in medical AI strives to innovate, with multiple groundbreaking ventures sparking a national healthcare revolution. But there is still a vital role for people to play. AI is not meant to replace trained healthcare providers, but rather to improve their ability with real-time insights. It all begins with and depends upon high-quality image annotation.

Develop AI-based medical care

In medical AI, its technology produces high-quality medical annotation knowledge which would, in fact, helps to create state-of-the-art CV prototypes that are useful in evaluating patient records to find cures. It increases the capacity of doctors to interpret the medical images.

Performance Control

AI creates the learning system to find through the finest quality strategy for Image Annotation applications.

The training evaluation software allows the accuracy of the highest standard that help to create state-of-the-art Image Annotation applications.

Magnetic Resonance Imaging (MRI)

Study of medical imaging by annotation of MRI images and supervision of deep learning techniques leading up for Medicare. Using the annotated MRI or CT images, establish computational equations for adaptive interpretation of a specific illness.

Radiology

Create deep learning techniques which decode images or videos from annotated radiology. Image annotation is the method of aligning the entire picture with a marker tag, or a part of an image. Using the open-source software and image annotation software, gaining practical knowledge will offer a range of image annotation service that suits the needs of the task, including object classes, 3D cuboids, lines and polynomials, textual categorization, byte/pixel segmentation, geometric shapes, object recognition, and so on.

Medical imaging is little more than a technique that is used in the benefit of clinical research and treatment procedure to build a visual image of the interior of the patient’s body. Medical imaging essentially creates a certain kind of repository of natural human anatomy to classify the body anomalous behavior. And every type of AI-backed advancements in healthcare imaging offers specific details about the body region being examined or addressed, ailments, and how successful medical treatment is in healing the ailments.

In image annotation, AI technology is used to look for patterns fitted with hundreds of computational techniques to automatically identify these patterns from the most common diseases and to show the type of condition in the body with absolute precision. Google recently developed AI-based algorithms that can predict the likelihood of death of the patient in hospitals with an accuracy of 95 percent enabling doctors to make accurate, appropriate actions.

How can AI introduce Medical Imaging Revolution?

Due to numerous obstacles in the field of medical imaging, AI is changing the healthcare sector in a better direction. There we’ll explore how AI can revolutionize medical imaging and make the process of medical care and diagnosis more reliable and supportive.

Image Annotation with Automatic Representation

AI-enabled apps can be able to annotate images of diagnostic imaging with body condition. And it will also automatically produce a plan after full review and interpretation of the data, based on its image processing technologies. At present, these tasks are typically undertaken by humans, so predicting the precise consequences may be a very complex task for machines. With more developments in AI-enabled medical imaging, nevertheless, image processing with machines would become more reliable and precise making it easier for physicians to make decisions and provide patients with the right care at a reasonable cost.

Radiologists must evaluate smarter

While humans can make these assessments smarter than robots, their decisions can often be influenced till a certain degree related to mental or indulgent causes. Meanwhile, Image annotation of AI-backed digital images can help to reduce errors and inconsistencies, particularly when documenting and evaluating. So, using the right database so artificial intelligence analytics with appropriate healthcare training images may be another way of minimizing errors. And the cost of negligence by making quick and better decisions by the radiologist therefore saving time and expense of all laboratories.

Source: Healthcare Guys

A joint research team led by Dr. Wang Kai and Dr. Du Jiulin from the Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences reported a novel volumetric imaging method—confocal light field microscopy (Confocal LFM)—to image fast neural and vascular dynamics at high speed, deep in the brain.

The study was published in Nature Biotechnology on August 10.

Traditional tools developed for in-vivo imaging in the brain, such as confocal microscopy and two-photon scanning microscopy, are based on point scanning scheme and are too slow to study rapid volumetric dynamics.

Light field microscopy (LFM) has attracted attention due to its instantaneous volumetric imaging capability, which captures the information from the whole 3-D volume all together on the image sensor in a single camera exposure.

Conventional LFM has been previously introduced to image brains. However, the technology has two major problems: reconstruction artifacts and lack of optical sectioning capability, preventing them from wide application.

In 2017, Wang's group developed a new type of LFM, XLFM, to address the first problem. However, the second one remained unsolved.

Introducing optical sectioning capability in LFM is of key importance because it prevents out-of-focusing background from interfering with in-focusing signal and plays a vital role for thick tissue imaging.

Directly adopting the concept of confocal detection that confers 3-D imaging capability seems to be straightforward. However, it turned out to be impossible due to the very distinct instantaneous volumetric imaging manner of LFM from conventional plane-by-plane imaging scheme.

To meet this challenge, Wang's lab came up with a novel idea to generalize the concept of confocal detection and made it compatible with LFM without compromising its imaging speed, which was called Confocal LFM.

Without interfering background noise, confocal LFM significantly improved imaging resolution and calcium signal detection sensitivity in functional imaging of neuronal activities over the whole zebrafish brain.

It was also integrated with a fast 3-D tracking system to capture correlated behavioral neural activations in freely swimming larval zebrafish. For example, single-neuron activities during larval zebrafish's prey capture could be reliably recorded.

The researchers further applied confocal LFM on imaging calcium transients and circulating blood cells in awake mouse brain. It's highly parallelized and light-efficient imaging enabled continuous recording for more than 100,000 volumes without significant photobleaching.

They also showed imaging of populations of circulating blood cells in a complicated 3-D vascular network. The results showed that it was possible to track and quantify the flow dynamics of blood cells accurately and represented more than 100 times increase in throughput compared to conventional imaging techniques.

The flexibility, increased resolution and sensitivity of confocal LFM demonstrated in this work make it promising for wide applications in capturing and studying extremely fast volumetric dynamics deep in the brain, or within other types of thick tissues in general.

Source: PHYS

Traditional single-cell sequencing methods help to reveal insights about cellular differences and functions—but they do this with static snapshots only rather than time-lapse films. This limitation makes it difficult to draw conclusions about the dynamics of cell development and gene activity. The recently introduced method 'RNA velocity' aims to reconstruct the developmental trajectory of a cell on a computational basis (leveraging ratios of unspliced and spliced transcripts). This method, however, is applicable to steady-state populations only. Researchers were therefore looking for ways to extend the concept of RNA velocity to dynamic populations which are of crucial importance to understand cell development and disease response.

Single-cell velocity

Researchers from the Institute of Computational Biology at Helmholtz Zentrum München and the Department of Mathematics at TUM developed 'scVelo' (single-cell velocity). The method estimates RNA velocity with an AI-based model by solving the full gene-wise transcriptional dynamics. This allows them to generalize the concept of RNA velocity to a wide variety of biological systems including dynamic populations.

"We have used scVelo to reveal cell development in the endocrine pancreas, in the hippocampus, and to study dynamic processes in lung regeneration—and this is just the beginning," says Volker Bergen, main creator of scVelo and first author of the corresponding study in Nature Biotechnology.

With scVelo researchers can estimate reaction rates of RNA transcription, splicing and degradation without the need of any experimental data. These rates can help to better understand the cell identity and phenotypic heterogeneity. Their introduction of a latent time reconstructs the unknown developmental time to position the cells along the trajectory of the underlying biological process. That is particularly useful to better understand cellular decision making. Moreover, scVelo reveals regulatory changes and putative driver genes therein. This helps to understand not only how but also why cells are developing the way they do.

Empowering personalized treatments

AI-based tools like scVelo give rise to personalized treatments. Going from static snapshots to full dynamics allows researchers to move from descriptive towards predictive models. In the future, this might help to better understand disease progression such as tumor formation, or to unravel cell signaling in response to cancer treatment.

"scVelo has been downloaded almost 60.000 times since its release last year. It has become a stepping-stone tooltowards the kinetic foundation for single-cell transcriptomics," adds Prof. Fabian Theis, who conceived the study and serves as Director at the Institute for Computational Biology at Helmholtz Zentrums München and Chair for Mathematical Modeling of Biological Systems at TUM.

Source: PHYS

Telehealth is the way forward and digital services will close the loop between consultation and services, writes Meera Murugesan

THE World Health Organisation describes telehealth as the use of telecommunications and virtual technology to deliver healthcare outside of traditional healthcare facilities.

It extends the reach of high-quality care and in-depth expertise to places like the home, as well as to remote and underserved communities.

The Covid-19 pandemic has shown that healthcare is in need of not just tweaking, but significant change.

A totally different approach to how healthcare is organised, delivered and distributed will be paramount post Covid-19, says Philips Malaysia country manager Muhammad Ali Jaleel.

Beyond this pandemic, hospitals around the world will face and continue to face burdens in terms of capacity, especially in ageing societies, he explains.

"One of the most apparent shifts we've seen is how Covid-19 is spurring the move to telehealth. To help deliver quality care and minimise risk to patients and staff, telehealth capabilities and digital technology that allow virtual care and remote patient monitoring are on the rise," he says.

While the industry is still relatively new, it is estimated that the global telehealth market will reach US$19.5 billion by 2025.

With the ability to change the current dynamics of healthcare delivery and make way for improved access and outcome in cost effective ways, telehealth allows remote patients to obtain clinical services.

GAME CHANGER

Telehealth enables a limited pool of healthcare professionals to reach and care for a large number of patients at different physical locations.

Take Covid-19 as an example, says Muhammad Ali.

Since Covid-19 is predominantly a respiratory illness, patients with more severe cases may require ICU care.

In the intensive care unit, a scarcity of critical-care intensivists and bed availability can compromise efficient patient flow throughout the hospital.

Healthcare institutions need digital tools to manage the increased patient flow resulting from the Covid-19 outbreak and dedicated, scalable telehealth solutions that facilitate the use of online screening, follow-up questionnaires and monitoring, and external call centre collaboration.

These remote screening solutions support healthcare institutes to diagnose and treat patients at alternative points of care and help safeguard the scarce critical care capacity.

For example, tele-ICU or eICU enables a co-located team of intensivists and critical care nurses to remotely monitor patients in the ICU regardless of patient location.

Intensivists and nurses based in a telehealth eICU hub are supported by high-definition cameras, telemetry, predictive analytics, data visualisation and advanced reporting capabilities in order to support their frontline colleagues.

They help care teams to proactively intervene at an earlier stage or to decide which patients have stabilised and can be transferred, allowing limited ICU beds to be allocated to more acute patients.

A whole range of innovative new ideas and coping strategies are currently being tested in different countries around the world, says Muhammad Ali.

WHAT TO EXPECT

In the near future, we could see digital services closing the loop between consultations and the dispatch of care or prescription drugs — drones as vehicles for getting drugs to patients or robots disinfecting contaminated areas, apps and chat-bots that act as symptom checkers and provide up-to-the-minute travel and infection control advice, medical wearables that monitor patients at home and 5G-enabled cameras that check for symptoms in seconds.

Muhammad Ali says although these innovations won't play global roles in the situation we are in right now, we should keep an eye on these developments.

It may well be that many health systems go back to the drawing board to improve their care based on today's experiences.

Covid-19 shook the foundation of healthcare systems around the world – some were more prepared than others, he adds.

The pandemic not only highlighted the significant gaps in technological adoption, funding and support, but it also demonstrated how the healthcare industry can benefit substantially from the adoption of readily available technology.

"We are now in the fortunate position that restrictions are being lifted gradually, but our commitment to improving the healthcare industry should not waver especially with an ageing population and rising chronic diseases. Embracing innovation is essential in the healthcare sector. Only this way, we can future-proof our healthcare system."

TELEHEALTH FOR HOSPITAL AND HOME

PHILIPS' telehealth solutions cover two aspects.

First, hospital telehealth. It's in-hospital telehealth programmes support advanced care delivery models through a unique combination of technology, clinical expertise and support that enables improved clinical and financial outcomes.

The second aspect is home telehealth, where Philips ambulatory telehealth programmes provide a daily connection between post-acute caregivers and patients, utilising technology and clinical processes to expand access, improve outcomes and provide a better experience for patients.

Source: NEW STRAITS TIMES

Proteins are essential to the life of cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate.

In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.

By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemistries so well that they rivaled those found in nature.

"We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

The results were published July 24 in the journal Science

Using artificial intelligence to learn design rules

Proteins are made up of hundreds or thousands of amino acids, and these amino acid sequences specify the protein's structure and function. But understanding just how to build these sequences to create novel proteins has been challenging. Past work has resulted in methods that can specify structure, but function has been more elusive.

What Ranganathan and his collaborators realized over the past 15 years is that genome databases--which are growing exponentially--contain enormous amounts of information about the basic rules of protein structure and function. His group developed mathematical models based on this data and then began using machine-learning methods to reveal new information about proteins' basic design rules.

For this research, they studied the chorismate mutase family of metabolic enzymes, a type of protein that is important for life in many bacteria, fungi, and plants. Using machine-learning models, the researchers were able to reveal the simple design rules behind these proteins.

The model shows that just conservation at amino acid positions and correlations in the evolution of pairs of amino acids are sufficient to predict new artificial sequences that would have the properties of the protein family.

"We generally assume that to build something, you have to first deeply understand how it works," Ranganathan said. "But if you have enough data examples, you can use deep learning methods to learn the rules of design, even as you are understanding how it works or why it's built that way."

He and his collaborators then created synthetic genes to encode for the proteins, cloned them into bacteria, and watched as the bacteria then made the synthetic proteins using their normal cellular machinery. They found that the artificial proteins had the same catalytic function as the natural chorismate mutase proteins.

A platform to understand other complex systems

Because the design rules are so relatively simple, the number of artificial proteins that researchers could potentially create with them is extremely large.

"The constraints are much smaller than we ever imagined they would be," Ranganathan said. "There is a simplicity in nature's design rules, and we believe similar approaches could help us search for models for design in other complex systems in biology, like ecosystems or the brain."

Though artificial intelligence revealed the design rules, Ranganathan and his collaborators still don't fully understand why the models work. Next they will work to understand just how the models came to this conclusion. "There is much more work to be done," he said.

In the meantime, they also hope to use this platform to develop proteins that can address pressing societal problems, like climate change. Ranganathan and Assoc. Prof. Andrew Ferguson have spun out a company called Evozyne that will commercialize this technology with applications in energy, environment, catalysis, and agriculture. Ranganathan has worked with UChicago's Polsky Center for Entrepreneurship and Innovation to file patents and license the IP to the company.

"This system gives us a platform for rationally engineering protein molecules in a way that we always dreamed we could," he said. "Not only can it teach us the physics of how proteins work and how they evolve, it can help us find solutions for issues like carbon capture and energy harvesting. Even more generally, the studies in proteins might even help teach us how the deep neural networks behind modern machine learning actually work."

Source: EurekAlert!

The coronavirus pandemic has created a paradigm shift in imaging. Not only have imaging centers faced the need for rapid implementation of strict protocols for patient management, decontamination of equipment and social distancing, these centers have also experienced steep declines in the number of studies being performed.

The decrease in imaging studies across the U.S. is estimated to have reached 63.6% in April 2020.1 But, some areas have been hit harder. In New York City, for example, the combined volume of CT and MRI cases declined by an average of 65% (range, 51%-80.9%) across five major academic centers as compared to prior year.2

Overall, the largest decreases in imaging studies have occurred in the elective, outpatient setting (70%), given the risk to patients, technologists and staff. However, inpatient and emergency room imaging studies have also declined by about 50%.3

As healthcare providers in many states are gaining some control over the COVID-19 crisis, the demand for most imaging services should rebound as postponed, but necessary imaging studies are rescheduled. Outpatient imaging centers are reopening to a “new normal” where enhanced decontamination and hygiene protocols, as well as personal protective equipment, will remain in place for the foreseeable future.

The backlog of studies will require imaging centers to be nimble in how they make up for lost time and revenue, which may include adding hours to fit more patients per day. However, this comes at a price to the radiologists who will feel the burden of longer hours, added workload and strain on cognitive functions. More than ever, radiologists will need solutions that alleviate their workload while maintaining the highest levels of precision in imaging interpretation.

To ensure imaging centers are well positioned for the coming transition and well beyond, digital solutions with artificial intelligence are a must-have. AI-powered digital solutions can aid imaging centers in managing workload via automation, enabling image interpretation and improving efficiency. These solutions can seamlessly integrate into the clinical workflow to alleviate the burden of repetitive tasks and amount of correction steps, which in turn help the radiologists improve their diagnostic accuracy.

All these capabilities enable the individual radiologists within the radiology practice to work more efficiently to drive workload and revenue potential. But, more importantly, the AI-powered algorithms and automation enable the radiologists to spend the needed time and focus on the clinically complex cases, as well as help to increase diagnostic precision for interpreting medical images.

The ramifications of the COVID-19 pandemic will be felt for some time. But, whether in the short or long term, imaging centers will benefit from solutions that enable automation and workflow efficiencies, reduce variability and improve precision. These solutions will ensure the “new normal” holds a bright future.

Source: HealthcareITNews

A research group led by Daniel Aili, associate professor at Linköping University, has developed a bioink to print tissue-mimicking material in 3D printers. The scientists have developed a method and a material that allow cells to survive and thrive.

"Bioprinting is a new and exciting technology to manufacture three-dimensional tissue-mimicking cell cultures. It has been a major problem to develop the bioink required, i.e. a material that can encapsulate the cells and be used in printers. Our bioink has several exciting properties that open new opportunities to approach our vision - creating tissue and organs in the laboratory", says Daniel Aili, associate professor in the Division of Biophysics and Bioengineering at Linköping University.

The properties of the ink can be modified as required and they have achieved excellent results in tests when using the material with different cell types: liver cells, heart cells, nerve cells and fibroblasts (a type of cell found in connective tissue). The research group has also solved one of the major challenges when attempting to print organic material: they have found a method that allows the cells to survive and thrive, despite the harsh treatment they receive. The results have just been published in the journal Biofabrication.

The ink the group has developed contains hyaluronan and synthetic molecules similar to proteins, known as peptides. These are bound together in a water-rich network, a hydrogel, that functions as a scaffolding for the cells.

"We can use some advanced chemical techniques to control how rapidly the hydrogel forms, in other words the transition from liquid to a gel that gently encapsulates the cells", says Daniel Aili.

The scientists have developed a modular system, like Lego bricks, in which different components can be combined to create different types of hydrogel. The hydrogels provide mechanical support to the cells and encapsulate them without damaging them. They can also control cell growth and behaviour. A system of various peptides makes it possible to modify the properties to control the cells and incorporate various functionalities. One example from the wide array possible is to attach an enzyme that stimulates the growth of bone.

"We are one of the first research groups that can change the material properties both before and after it is printed. We can, for example, increase the degree of cross-linking during the process to provide more stability to the material, and we can change the biochemical properties. We can also adapt the material to different types of cells. This is a further step on the way to mimicking the support structures that surrounds most human cells, the extracellular matrix", says Daniel Aili.

Since the material is dynamic and can be given tailored properties when used as bioink in 3D printing, the result of the research is referred to as a 4D printed biomaterial - yet another step closer to mimicking the body's own functions.

"Our work is quite basic research, but we are aware that there is a huge medical need for tissue, and for better and biologically relevant models for drug development, not least as a replacement for animal experiments. Progress is rapid in this field at the moment", Daniel Aili concludes.

The research has received funding from, among other sources, the Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF), and the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linköping University (AFM) at Linköping University.

Source: EurekAlert!

Researchers have developed a human cell 'membrane on a chip' that allows continuous monitoring of how drugs and infectious agents interact with our cells, and may soon be used to test potential drug candidates for COVID-19.

The researchers, from the University of Cambridge, Cornell University and Stanford University, say their device could mimic any cell type--bacterial, human or even the tough cells walls of plants. Their research recently pivoted to how COVID-19 attacks human cell membranes and, more importantly, how it can be blocked.

The devices have been formed on chips while preserving the orientation and functionality of the cell membrane and have been successfully used to monitor the activity of ion channels, a class of protein in human cells which are the target of more than 60% of approved pharmaceuticals. The results are published in two recent papers in Langmuir and ACS Nano.

Cell membranes play a central role in biological signalling, controlling everything from pain relief to infection by a virus, acting as the gatekeeper between a cell and the outside world. The team set out to create a sensor that preserves all of the critical aspects of a cell membrane--structure, fluidity, and control over ion movement--without the time-consuming steps needed to keep a cell alive.

The device uses an electronic chip to measure any changes in an overlying membrane extracted from a cell, enabling the scientists to safely and easily understand how the cell interacts with the outside world.

The device integrates cell membranes with conducting polymer electrodes and transistors. To generate the on-chip membranes, the Cornell team first optimised a process to produce membranes from live cells and then, working with the Cambridge team, coaxed them onto polymeric electrodes in a way that preserved all of their functionality. The hydrated conducting polymers provide a more 'natural' environment for cell membranes and allows robust monitoring of membrane function.

The Stanford team optimised the polymeric electrodes for monitoring changes in the membranes. The device no longer relies on live cells that are often technically challenging to keep alive and require significant attention, and measurements can last over an extended time period.

"Because the membranes are produced from human cells, it's like having a biopsy of that cell's surface - we have all the material that would be present including proteins and lipids, but none of the challenges of using live cells," said Dr Susan Daniel, associate professor of chemical and biomolecular engineering at Cornell and senior author of the Langmuir paper.

"This type of screening is typically done by the pharmaceutical industry with live cells, but our device provides an easier alternative," said Dr Róisín Owens from Cambridge's Department of Chemical Engineering and Biotechnology, and senior author of the ACS Nano paper. "This method is compatible with high-throughput screening and would reduce the number of false positives making it through into the R&D pipeline."

"The device can be as small as the size of a human cell and easily fabricated in arrays, which allows us to perform multiple measurements at the same time," said Dr Anna-Maria Pappa, also from Cambridge and joint first author on both papers.

To date, the aim of the research, supported by funding from the United States Defense Research Projects Agency (DARPA), has been to demonstrate how viruses such as influenza interact with cells. Now, DARPA has provided additional funding to test the device's effectiveness in screening for potential drug candidates for COVID-19 in a safe and effective way.

Given the significant risks involved to researchers working on SARS-CoV-2, the virus which causes COVID-19, scientists on the project will focus on making virus membranes and fusing those with the chips. The virus membranes are identical to the SARS-CoV-2 membrane but don't contain the viral nucleic acid. This way new drugs or antibodies to neutralise the virus spikes that are used to gain entry into the host cell can be identified. This work is expected to get underway on 1 August.

"With this device, we are not exposed to risky working environments for combating SARS-CoV-2. The device will speed up the screening of drug candidates and provide answers to questions about how this virus works," said Dr Han-Yuan Liu, Cornell researcher and joint first author on both papers.

Future work will focus on scaling up production of the devices at Stanford and automating the integration of the membranes with the chips, leveraging the fluidics expertise from Stanford PI Juan Santiago who will join the team in August.

"This project has merged ideas and concepts from laboratories in the UK, California and New York, and shown a device that works reproducibly in all three sites. It is a great example of the power of integrating biology and materials science in addressing global problems," said Stanford lead PI Professor Alberto Salleo.

Source: EurekAlert!

Health care providers and payors remain immersed in the pandemic. Deaths and hospital admissions hover well above normal levels, requiring emergency response measures to manage and care for thousands of patients.

Health care organizations, understandably, are in a reactive stance. In addition to grappling with the immediate response to the Covid-19 outbreak, a forecasted second spike in illnesses and hospitalizations looms large.

However, this emerging window of opportunity creates a pause-and-reflect-moment to enhance and coordinate short-term measures taken in response to the pandemic. Additionally, the long-term environment is now more fully primed for digital health’s more central, dynamic role in meeting consumers’ evolving health care expectations.

Challenges and solutions – Right now

Health care has been stretched to new limits in patient volume, equipment shortages, and the sheer number of calls, emails, and inquiries coming from the patients/members, media, and government. Fortunately, digital health technology has quickly proven itself powerful in a wide range of use cases:

Triage: Critical care needs demand priority attention. AI-enabled chatbots and interactive voice response (IVR) have helped redirect the brunt force of phone calls and emails going in and out of medical offices and call centers, allowing staffing reallocations to what matters most: rapid and nimble health delivery.

Care: Virtual and televisits, follow-ups, and consults redirect the number of in-person visits for patients with non-urgent symptoms. This proves particularly helpful when patients can be effectively treated at home, thus limiting exposure. Additionally, remote monitoring and support of high-risk populations such as the elderly, immunocompromised, obstetric patients, and those with comorbidities reduces risk of virus spread.

Communication: Gathering and analyzing critical information is important in prioritizing those who are high-risk and providing adequate resources where necessary. The pandemic also heightened the need to automate the distribution of educational materials, guidelines, and frequently asked questions (FAQ) to patients, health care members, and communities – not to mention providing real-time communication and educational updates to health care professionals on the latest hospital protocols and policies.

So what has helped health care organizations most rapidly respond to Covid-19? Omnichannel, digitally centric interactions that draw upon the real-time power of AI.

Automate user interaction with chatbots

Chatbot tools and services automate user interaction, easing the burden on customer service and health care teams overwhelmed by high volumes of inquiries. Quick to implement – often within days or weeks – and easy to custom-configure, these technologies help triage patients, automatically guide non-critical patients/members to the most helpful resources, and greatly reduce call wait times. They also adhere to major medical and data compliance and regulatory requirements, including HIPAA.

Cultivate rapid, tailored outreach powered by CRM

CRM systems have proven invaluable in responding immediately during a pandemic. They not only offer a single view into patients and members, but can also analyze specific patients, automate and manage workflows, and – when integrated with the appropriate tools – be used to educate and inform.

Boost existing resources with powerful enhancements

In addition to the capabilities and technologies that can be rapidly designed and implemented, health care providers and payors can also tap into their existing infrastructure. Adapting your approach to digital communication, marketing, and enhancing your search capabilities can improve the digital experience.

Search connectors: Solutions that enhance the search experience for employees and patients/members can be quickly implemented using a variety of technologies like Google Connector and Coveo that integrate across multiple channels and access information stored in multiple data repositories. An enhanced search capability quickly and comprehensively equips customer service agents with relevant information and knowledge from a single search, anywhere – a resource of particular benefit during high call volumes demanding quick resolutions. Additionally, this same resource better empowers patients/members in their own searches on your digital properties.

Inclusive messaging: Consider all audiences such as non-English-speaking and those who have other health issues (diabetes, cancer, cardiology, pregnancy, urgent care needs), and many more who still need health care and are wondering how Covid-19 will impact their care.

Diversion strategy search engine marketing (SEM) and opportunity analysis: Search teams are now working alongside providers to successfully leverage paid search in routing non-Covid-19 patients to urgent care facilities. These urgent care “campaigns in a box” have been helpful in getting people urgent and emergent medical care while keeping emergency rooms clear for the most critical patients and to reduce levels of risk for the virus.

Paid media opportunity analysis and campaign support: As Covid-19 causes significant shifts in consumer behavior, marketing teams should analyze and respond to where and how members/patients are now spending their time. By adjusting strategies and campaigns to promoting Covid-19-related content where your target audience is now the most active and engaged, you’ll ensure you can distribute information with maximum exposure. Offerings include augmenting your team to rapidly deploy new campaigns.

Website survey feedback loop: Consumers are looking to health care organizations to provide guidance and answer key questions during this pandemic. Organizations are working hard to answer those questions but may be missing some key questions, especially questions that may be regional in nature.

Virtual care options: Help consumers navigate care solutions you have available by defining virtual care, promoting virtual care, and providing an exceptional virtual care experience making the most of your digital infrastructure.

As the new normal emerges, there will be a broader role for digital coordination and management in health care, encompassing more than just virtual visits.

Source: DALLAS Business Journal

Students at Cranfield University have designed computer models that can identify COVID-19 in X-rays. The models use computer vision and artificial intelligence (AI) to analyse chest X-ray imagery. It can classify information which would not normally be recognised with the naked eye and assist with the diagnosis of COVID-19.

A common symptom of COVID-19 is pneumonia. The AI is able to detect anomalies in an X-ray, classifying which are positive for pneumonia, then a second model is used to diagnose if the pneumonia is caused by the COVID-19 virus.

Two groups studying for their MSc programme, specialising in Computer and Machine Vision (CMV option), decided to take up this challenging topic as their group project. The group project provides the students with the opportunity to work collaboratively on problems and to devise a solution.

This year the group project activity was itself impacted by COVID-19 and, due to lockdown, some students returned to their homes overseas. The determined groups continued with their projects remotely, despite being thousands of miles apart in China and France, as well as nearby Cranfield and Milton Keynes. The video conferencing and IT facilities provided by the University to the students was vital in allowing access to the necessary computational resources, ensuring the continuation and success of their research.

The lack of X-ray imagery in the public domain containing COVID-19 details was a challenge—however, the teams were able to build detailed information from various sources.

The groups employed conventional machine learning algorithms as wells as deep learning frameworks, a machine learning technique that teaches computers to learn by example. The AI model employed in this project was able to predict results with great accuracy. However, the research teams believe that they are able to further develop new algorithms to produce even more robust and reliable results.

The teams are led by Dr Zeeshan Rana, Lecturer in Computational Engineering at Cranfield University. He is now exploring collaboration opportunities with medical authorities or industry to develop the project to the next level, using more advanced AI algorithms and CT (computed tomography) scans to show greater detail and accuracy in the results.

Dr Zeeshan Rana said: "The research carried out in this pilot project has led to some extremely promising results and we are looking to build on this success rapidly to help in the fight against COVID-19. I am incredibly proud of the work my researchers have carried out. They are a credit to the University and I'm delighted that we are able to support them remotely in carrying out their studies."

출처: Healthcare

Personal accessories such as glasses and watches that we usually carry in our daily life can yield useful information from the human body, yet most of them are limited to exercise-related parameters or simple heart rates. Researchers have developed smart electronic glasses (e-glasses) that not only monitor a person's brain waves and body movements but also function as sunglasses and allow users to control a video game with eye motions.

Devices that measure electrical signals from the brain or eyes can help diagnose conditions like epilepsy and sleep disorders, as well as control computers in human-machine interfaces. But obtaining these measurements requires a steady physical contact between skin and sensor, which is difficult with rigid devices. The research team from Korea University wanted to integrate soft, conductive electrodes into e-glasses that could wirelessly monitor brain and eye signals, UV intensity, and body movements or postures, while also acting as a human-machine interface.

According to the findings, published in the journal ACS Applied Materials & Interfaces, the researchers built the glasses' frame with a 3D printer and then added flexible electrodes near the ears (EEG sensor) and eyes (EOG sensor). They also added a wireless circuit for motion/UV sensing on the side of the glasses and a UV-responsive, colour-adjustable gel inside the lenses. When the sensor detected UV rays of a certain intensity, the lenses changed colour and became sunglasses.

The motion detector allowed the researchers to track the posture and gait of the wearer, as well as to detect when they fell. The EEG recorded alpha rhythms of the brain, which could be used to monitor health. Finally, the EOG monitor allowed the wearer to easily move bricks around in a popular video game by adjusting the direction and angle of their eyes. The e-glasses could be useful for digital healthcare or virtual reality applications, the researchers said.

출처: Medical Dialogues

안경과 시계와 같이 우리가 매일 착용하는 개인 액세서리를 통해 신체의 중요한 정보를 알 수 있지만, 여전히 대부분의 관련 데이터는 신체 움직임 및 간단한 심장 박동을 재는 것으로 제한되어 있다. 최근 연구진이 개발한 스마트 전자 안경은 뇌파 및 신체 움직임을 모니터링할 뿐만 아니라 선글라스로도 사용 가능하며 눈의 움직임으로 비디오 게임 조작도 할 수 있다.

뇌와 눈의 전자 신호를 측정하는 디바이스를 통해 간질 및 수면 장애 등과 같은 건강 상태를 진단할 수 있을 뿐만 아니라 사람과 기계의 인터페이스 (human-machine interfaces)에서 기계를 통제 할 수도 있다. 하지만 이런 측정 및 기능이 가능하려면 센서가 사람의 피부와 계속 접촉한 상태로 유지 되어야 하는데 표면이 단단한 장치는 이런 조건을 만족시키기 어렵다. 고려 대학교의 연구팀은 부드러운 전도성 전극을 스마트 전자 안경에 부착하여 무선으로 뇌와 눈의 신호, UV 강도, 신체 움직임이나 자세를 모니터하는 동시에 스마트 전자 안경으로 사람과 기계간의 인터페이스 기능을 제공하는 연구를 진행했다.

미국 화학회 응용 물질 & 인터페이스 (ACS Applied Materials & Interfaces) 저널에 발표된 연구 결과에 의하면, 연구진은 3D 프린터를 이용해서 안경의 프레임을 제작하고 안경의 귀 (뇌전도 (EEG) 센서)와 눈 (안전위도 (EOG) 센서) 근처에 유연한 전극을 부착했다. 안경의 옆 부분에 동작/UV 센서를 위해 무선 회로를 부착하고 렌즈 안에는 UV에 반응해 색이 변하는 젤 (gel)을 첨가했다. 안경 센서가 특정 강도의 UV 광선을 감지하면 렌즈의 색이 변해 선글라스가 된다.

연구진은 안경의 동작 감지 센서를 이용해 안경 착용자의 자세와 걸음걸이뿐 아니라 안경이 떨어졌을 때도 추적할 수 있었다. 뇌전도 (EEG) 센서는 건강 모니터링에 사용 가능한 뇌의 알파 리듬을 기록했다. 마지막으로, 안경 착용자는 안전위도 (EOG) 센서를 부착한 모니터를 이용해 눈의 방향과 각도 조절로 비디오 게임에서 벽돌을 쉽게 옮길 수 있었다. 스마트 전자 안경은 디지털 건강 관리 프로그램 또는 가상 현실 응용 프로그램에 유용하게 쓰일 수 있다고 연구진은 말했다.

출처: Medical Dialogues

Technology isn't just for video games and social media. It can also give you access to important services, like medical aid. With telehealth, you're able to check in with healthcare professionals from the comfort of your own home, and that's just the start.

Here's why telehealth is deemed by many as the future of health care — and how you can make the most of all these digital services to stay healthy and safe.

What is telehealth?

Telehealth is the remote access of health care services with telecommunications technology, such as computers and mobile devices.

There are many services that fall under telehealth, but the one you're most likely to use is called telemedicine.

The difference between telehealth and telemedicine

Telemedicine is what allows you to receive remote clinical services. For example, this would include a virtual appointment with your doctor or digital access to your medical records.

Telehealth, on the other hand, encompasses services that medical professionals are more likely to utilize, such as provider training, administrative meetings, or medical education.

So, while telehealth broadly refers to almost any digital activities for health care workers, telemedicine is patient-facing and the practice of delivering care remotely.

Examples of telehealth services

These are some of the most common and useful telehealth services for patients:

Patient portal

Many health care providers now offer online patient portals. In fact, you may already utilize this telehealth tool.

With a patient portal, you can:

 Access your personal health record, including previous test results and medical history.

 Schedule new appointments and request email reminders for them.

 Communicate securely with your primary care provider, such as a doctor or nurse.

 Request refills for existing prescription medications.

Most patient portals will require you to log on with a personalized account on a computer, tablet, or smartphone. From there, you should be able to access all these telehealth services in one centralized location.

Virtual doctor's appointments

Patients and doctors can also virtually meet "face-to-face" for medical appointments with secure live video conferencing.

These virtual doctor's appointments are useful for patients with limited mobility, or those who don't live very close to a doctor's office. And, because of the COVID-19 pandemic, many more patients are opting to schedule virtual visits to reduce the risk of spreading the virus.

Virtual appointments can be effective for any condition that doesn't require an in-person physical examination. This may include:

Mental health counseling and therapy

Dermatology and skin conditions

Allergies and sinus or nasal congestion

Some other common illnesses, such as headaches or pink eye

Here's how to set up and prepare for a virtual doctor's appointment:

First, you should ensure that your health insurance covers telehealth services in your state. To do so, you can use the National Telehealth Policy Resource Center's interactive guide for current state laws and reimbursement policies.

Then, you'll want to schedule an appointment by calling your doctor's office or using an online patient portal. Make sure you have access to a computer, tablet, or phone with live video conferencing, and find a quiet environment with good lighting to establish smooth communication.

From there, your virtual appointment should be pretty similar to a regular one — you'll discuss your symptoms and medical history, and your doctor can recommend treatments and sometimes prescribe medication.

If you need further physical examination, your doctor may ask to schedule a future in-person appointment.

Store and forward

Store and forward services, also called asynchronous telemedicine, are used to send information between a patient and doctor, or between doctors.

For example, after you have an X-ray, your primary care doctor might send that X-ray image to a specialist to help decide your diagnosis. This can allow patients to get faster, high-quality care even if experts aren't available locally.

With store and forward, the electronic transmission of medical documents is private and secure. Along with X-rays, doctors can send MRIs, photos, video clips, or other patient data.

Remote patient monitoring

Telehealth technology can allow doctors to monitor your health remotely, whether you've just been released from the hospital or you're managing a chronic condition.

Remote patient monitoring may include the use of:

Medical devices that can measure and send information to your doctor, such as cuffs to check blood pressure at home.

Wearable devices that automatically record and transmit vital information to your doctor, such as heart rate or sleeping patterns. These will be used when your doctor needs real-time information about your condition.

Home monitors that can detect falls or changes in normal activity. These can be used for elderly or disabled people who want to live at home but need frequent health check-ins.

Mobile health

Mobile health can help you keep track of your health over time. It's estimated that more than 40% of people now use at least one of the many different types of mobile health apps.

For example, some mobile health apps help you better manage chronic conditions like diabetes, or pursue fitness goals while recording your progress.

Other mobile health apps offer extensive networks of doctors across specialties, where medical professionals are available 24/7 and can give personalized advice in multiple languages.

To learn more about receiving care through mobile health apps, our Insider Reviews team has put together a list for the best telemedicine services, as well as the best online therapy providers for mental health needs.

Benefits of telehealth

Because of telehealth's benefits, many more hospitals have begun offering these services in the past decade.

For patients, the benefits of telehealth include:

Better access to medical care

Telehealth is especially advantageous for people in rural areas who don't live near a doctor or specialist, as it breaks down the geographic barriers to receiving care.

In addition, people with limited mobility, such as elderly or disabled people, don't need to go to the doctor as frequently. With telehealth, it's easy to access care from your own home.

Improves quality of care

In fact, a 2015 study published in the American Journal of Managed Care found that telehealth can lead to better patient health outcomes:

38% fewer hospital admissions

31% fewer hospital readmissions

63% more likely to spend fewer days in the hospital

Reduces cost of care

By reducing the need for travel and repeat hospital visits, telehealth can help patients save money.

For example, some doctors have found that telehealth care can cost 19% less when compared with inpatient care.

Increases patient satisfaction

Telehealth care also saves time. With more convenience and flexibility, children won't have to miss as much school and parents won't have to take as much time off work.

Overall, telehealth makes it easier for patients to receive care and stay healthy, resulting in higher satisfaction.

What is telepharmacy?

Telepharmacy is the delivery of pharmacy services to your home, and it's another useful aspect of telehealth.

After a telemedicine appointment, doctors will often have enough information to advise patients on which over the counter medications to take — or to write a prescription.

In fact, these common medications can be prescribed without ever seeing a doctor in-person:

 Antibiotics such as Penicillin, Amoxicillin, or Azithromycin

 Allergy medicine like Clartin or Flonase

 Asthma medications, such as Albuterol Inhalers or albuterol nebulizing solution

Medication can then be delivered to you through a local pharmacy like CVS, or by using a digital pharmacy app. To learn more about these apps, Insider Reviews has a list of the best pharmacy delivery services.

Digital and physical pharmacies can also deliver non-prescription medications like Advil, Tylenol, vitamins, and supplies like thermometers. Controlled substances, like painkillers or antidepressants, are more heavily regulated and may not be available for delivery.

Limitations of telehealth

While there are many benefits of telehealth, it cannot replace traditional medical care entirely.

For example, many medical conditions and routine check-ups will require a physical examination, and some patients may always feel more comfortable meeting with their doctor in-person. And though coverage is expanding, some insurers still don't provide for telehealth.

In addition, telehealth can lead to fragmented care if you use too many different services, as doctors may recommend unnecessary or overlapping treatments.

The bottom line

Overall, telehealth can support in-person medical care and provide another option for those who have limited access, or may not be able to physically visit a doctor.

You can check with your primary care provider and insurer to determine which telehealth services are readily available to you.

출처: INSIDER

A new wearable device that assesses motor fluctuations may be a useful tool for detecting dyskinesia in patients with Parkinson's disease, a new study suggests.

"The device, which is worn on the wrist like a watch, can tease out wearing off and dyskinesia and identify patients who need to see a neuro-specialist or who may need a neurosurgical procedure," lead author, Echo E. Tan, MD, Cedars-Sinai Medical Center, Los Angeles, told Medscape Medical News.

She explained that at present, clinicians rely on taking a history to try and identify those patients with more serious symptoms, but many patients are unable to communicate their symptoms and cannot keep reliable records.

"Symptom diaries are usually filled out by caregivers and are generally not very accurate. We need a more quantitative system and this device may help with that."

"We found the device gave a more accurate assessment of symptoms than patient diaries, and the device was used more consistently than the diaries," Tan added.

The study was published in Functional Neurology.

In the paper, the authors note that Parkinson's disease patients typically respond well to medical therapy in the first few years of their disease. However, approximately 40% of patients develop fluctuations of response to levodopa and dyskinesia after 4-6 years of treatment, which increases to 70% after longer-term treatment.

The current device, which has the brand name Personal KinetiGraph, was developed to address the lack of objective measurement tools for movement disorders. It quantifies symptoms, including tremor, bradykinesia, and dyskinesia, and produces a motor fluctuation score.

For the study, Tan and colleagues assessed 60 patients attending the Movement Disorders Clinic at Cedars-Sinai Medical Center with detailed questionnaires — Wearing Off Questionnaire (WOQ9) and Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part IV scores — and assigned them into four disease categories: mild (no fluctuations); mild with minor fluctuations; moderate fluctuations, and severe bothersome fluctuations. These fluctuations included wearing off and dyskinesia.

"We wanted to see if the device could distinguish between these different types of patients," Tan said.

The patients then wore the Personal KinetiGraph for 6 days and they or their caregivers also completed symptom dairies.

Results from the 54 subjects who completed the study showed that the fluctuation score on the device significantly differentiated between early fluctuators and troublesome fluctuators (P = .01), as well as dyskinetic and non-dyskinetic subjects (P < .005).

In contrast, motor diaries could not distinguish the four study groups on the basis of average OFF time, while average time with dyskinesia distinguished non-fluctuators and moderate fluctuators but did not distinguish among all four groups.

The device also identified high levels of dyskinesia in some patients who denied having dyskinesia.

"We found that the device fluctuation score can distinguish between patients who do and do not have dyskinesia — ie the two milder groups and the two more severe groups. But it couldn't distinguish between the two milder groups," Tan reported.

"The fluctuator score appears to be a good triage tool — it can help physicians identify patients with these motor fluctuations without spending long periods of time at the clinic."

Tan pointed out that the device can also be a source of additional information for physicians. "As well as the fluctuator score it also gives other useful information including a bradykinesia score (how slow the patient has been during the day); tremor responses to medications; how long the patients has been asleep — which can help work out if the medication is making them drowsy. It also gives medication reminders."

"It does take a bit of training to interpret the graphical data so it will probably be used mainly by movement disorder specialists and neurologists treating more severe Parkinson's patients," she commented.

Tan believes it will be particularly useful for patients starting to develop more serious symptoms. "For those patients who start to complain about a bit of wearing off and the problem is not solved in one clinic visit or with one medication change, we can use this device to get a handle on what is happening."

Another group who may benefit are those who live a long distance from a specialist clinic. "They could just come to the clinic a couple of times a year and use this device in between to monitor symptoms. It would also be good for patients with cognitive impairment or for those who don't have caregivers that can give reliable information," Tan suggested.

The Personal KinetiGraph device has been approved in the United States and Europe. It is still undergoing post-marketing clinical evaluation, but reimbursement codes are now available so it can be prescribed in the United States where it has a list price of around $700, manufacturer Global Kinetics said. It is also available in the United Kingdom, the Netherlands, and Germany.

Commenting for Medscape Medical News, James Beck, PhD, the Parkinson's Foundation chief scientific officer, said: "This study is another example of how, over time, we can expect to see more technology being brought to bear for the clinical assessment of motor disorders. The sensitivity to distinguish the various degrees of motor fluctuations is not yet there, so, to me, the benefit of this device is not everyday clinical practice but for clinical trials to reduce participant burden and improve reporting. As sensitivity/accuracy increases — along with ease of interpretation — we can expect to see wider deployment of devices like these in a clinical setting."

출처: Medscape

Gone are the days of shuffling papers and making phone calls or visits to collect and pass on results. With the introduction of mobile health operating systems, information can now be shared at the touch of a screen. As mobile health becomes more widespread, solutions are proliferating.

Through cloud computing, people can have seamless access to shared data, resources and common infrastructure.

Over the network, organizations can offer services on demand and carry out tasks that meet changing needs and standards. Electronic applications make it possible to do all this, and more, in the health care setting.

Mobile health, or mHealth, incorporates cloud computing technology and devices such as tablets, mobile phones and personal digital assistants (PDAs) for a variety of purposes.

But while it can make eHealth applications and medical information available anywhere at anytime, it must also be portable, secure and easy to use.

The range of applications and services supported by mHealth systems include:

    Mobile telemedicine, used for remote consultations

    Storing and sharing of patient data

    Personalized monitoring of vitals, now enhanced through interconnectedness with wearable devices

    Location-based medical services to ensure delivery of locally-relevant information

    Emergency response and management

    Pervasive access to health care information.

But as mobile technology gathers pace, the possibilities may be limited only to our imagination.

Advantages and challenges of mobile technology in health care

As governments and individuals experience ever-greater pressure to increase efficiency, mHealth solutions can offer numerous advantages.

The mobility of an interconnected, wireless system means that it can be used anywhere, and specifically at the point of care.

Collaboration can reduce the risk of errors: there is less physical paperwork to get lost and a reduced risk of two doctors making different decisions.

Point-of-care digital tools can help to safeguard patients and protect professionals against litigation through instant recording of data and potential for verification in real time and in the future.

mHealth can save time and money by enabling instant recording of information and a reduction in the duplication of tasks. It can enable virtual meetings, eliminating the need to move physically to a new location.

Pooling of data and resources can lead to closer collaboration and stronger teams. Professional development becomes more feasible due to instant, online delivery of research, training materials and other updates.

The challenges of mHealth solutions include the practicalities of data storage and management, availability and maintenance of the network, as well as compatibility and interoperability.

The biggest issue is perhaps security and privacy, raising questions about permission control, data anonymity and confidentiality, as well as the integrity of the infrastructure.

The initial financial outlay and training and resistance to change within an organization may pose further challenges.

A case in point: Medopad

To investigate mHealth solutions further, Medical News Today have been investigating a specific example, “Medopad,” recently represented at the “Internet of Things” conference and exhibition in London, UK.

Medopad is a mobile health operating system (mHOS™) that provides software, security, data connectivity and more to a number of major National Health Service (NHS) hospitals in London, amongst others.

We asked Jesko Bartelt, head of business operations at Medopad, about the concept.

Is Medopad a device or an app?

He told us that it is neither an app nor a device, but provides software solutions.

“Medopad is a mobile health platform that delivers […] workflow solutions that provide easy access to unified and comprehensive patient information across hospital departments and multiple locations.”

Additional functions include the incorporation of the Apple Watch to help manage chemotherapy treatment for cancer patients.

There is also a Google-glass app that surgeons can wear during surgery, enabling them to share a procedure for training purposes or consultation with other specialists who are not in the room.

How does Medopad work with existing systems?

Bartelt explained to MNT that Medopad integrates with and complements existing hospital systems, by collecting all data from hospital databases and collating them into a central source. Doctors can access this information quickly from an iPad.

One function might be a physician taking a photo of a patient’s visible symptoms and sharing them through the system with other professionals, who can then give advice.

How does Medopad deal with patient data?

Bartelt explained that Medopad does not contain data, but it provides access to a wide range of health care and patient data. Doctors can use it to interact with patient records, access lab results, view vital signs, take images and more.

The device means that hospitals can pool their patients’ data so it can be “served up to doctors on mobile devices in real-time.” Bartelt explains:

The Apple Watch apps, Bartelt said, allows doctors to connect with their patients to provide better support and ultimately better care.

출처: Medical News Today