"You had to decide for every single patient how you're going to provide care for them in a way you never had before," he recalls. That prompted him to ponder the role of the physical itself: "What would happen if I delayed it three months, or didn't do it at all?" For Dr. Hyman and many other physicians and their patients, the pandemic triggered a disruption in one of medicine's most common encounters--and, through virtual visits, provided an early glimpse of the physical of the future. A look at how innovation and technology are transforming the way we live, work and play. An explosion of advances in digital technology, imaging, gene sequencing and artificial intelligence will likely transform the physical into an even more virtual experience.

Summary: A new machine-learning algorithm which videos of echocardiograms is able to accurately predict patients who will die within a year. 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.

A team of scientists from Geisinger and Tempus have found that artificial intelligence can predict risk of new atrial fibrillation (AF) and AF-related stroke. Atrial fibrillation is the most common cardiac arrhythmia and is associated with numerous health risks, including stroke and death. The study, published in Circulation, used electrical signals from the heart--measured from a 12-lead electrocardiogram (ECG)--to identify patients who are likely to develop AF, including those at risk for AF-related stroke. "Each year, over 300 million ECGs are performed in the U.S. to identify cardiac abnormalities within an episode of care. However, these tests cannot generally detect future potential for negative events like atrial fibrillation or stroke," said Joel Dudley, chief scientific officer at Tempus.

Today on THCB Spotlights, Matthew speaks with Jeremy Orr, CEO of Medial EarlySign. Medial EarlySign does complex algorithmic detection of serious diseases, working on early detection of cancer and the progression of chronic disease such as diabetes. Tune in to hear more about this AI/ML company that has been working on their algorithms since before many had even heard about machine learning, what they've been doing with Kaiser Permanente and Geisinger, and where they are going next. Filmed at the HLTH Conference in Las Vegas, October 2019.

Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 -- November 16-18 in Philadelphia. The Association's Scientific Sessions is an annual, premier global exchange of the latest advances in cardiovascular science for researchers and clinicians. Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks -- advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted. "This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania. Researchers speculated that a deep learning model could predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops.

Deep neural networks identified potential adverse outcomes and atrial fibrillation from 12-lead ECGs that were originally interpreted as normal, according to new research presented at the American Heart Association Scientific Sessions. "Applications of machine learning and artificial intelligence techniques to problems in health care are increasingly common, but generally focus on diagnostic problems such as detecting features in an image of classifying a current diagnosis based on present features," Christopher M. Haggerty, PhD, assistant professor in the department of imaging science and innovation, and Brandon K. Fornwalt, MD, PhD, associate professor and director of the department of imaging science and innovation, both at Geisinger in Danville, Pennsylvania, told Healio. "Few studies have been able to apply machine learning to the task of predicting future events or patient outcomes. This work is among the first to demonstrate proof of concept for predicting a future patient event -- 1-year mortality -- with good performance based solely on 12-lead electrocardiography data." Sushravya M. Raghunath, PhD, math and computational scientist in the department of imaging science and innovation at Geisinger, and colleagues analyzed 1,775,926 12-lead resting ECGs of 397,840 patients from 34 years of archived medical records.

Is there anything today that can't possibly be done by Artificial Intelligence? From self-driving cars, 3D printing, sex robots that can breathe, and many other AI innovations, AI can do just about everything. To that end, researchers from Pennsylvania healthcare provider, Geisinger have trained an AI to predict which patients are at risk of dying within a course of a year, reports New Scientist. SEE ALSO: Chrome's New Feature Uses AI To Describe Images For Blind And Low-Vision Users Artificial Intelligence can reportedly determine when a person will die based on their heart test results, even if these results look normal to doctors. Dr. Brandon Fornwalt at the healthcare provider, Geisinger, trained the AI with examining 1.77 million electrocardiogram (ECG) results from almost 400,000 people to predict patterns that signal towards future cardiac issues.

Researchers from Pennsylvania healthcare provider Geisinger have trained an AI to predict which patients are at a higher risk of dying within the next year, New Scientist reports. They fed the AI 1.77 million electrocardiogram (ECG) logs -- measured in voltage over time -- from 400,000 patients, in order to detect patterns that could indicate future cardiac problems including heart attacks and atrial fibrillation. The results were impressive and a little scary. The AI model performed better than existing methods, according to the researchers, at distinguishing between patients who would die within a year and those who survived. "No matter what, the voltage-based model was always better than any model you could build out of things that we already measure from an ECG," Brandon Fornwalt, lead researcher of the study at Geisinger, told New Scientist.

Artificial intelligence can examine electrocardiogram (ECG) test results, a common medical test, to pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia) or of dying within the next year, according to two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019--November 16-18 in Philadelphia. Researchers used more than 2 million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks--advanced, multi-layered computational structures. Both studies, from the same group of researchers, are among the first to use artificial intelligence to predict future events from an ECG rather than to detect current health problems, the scientists noted. "This is exciting and provides more evidence that we are on the verge of a revolution in medicine where computers will be working alongside physicians to improve patient care," said Brandon Fornwalt, M.D., Ph.D., senior author on both studies and associate professor and chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania. Researchers speculated that a deep learning model could predict irregular heart rhythms, known as atrial fibrillation (AF), before it develops.

Out of more than 300 artificial intelligence proposals, the Centers for Medicare & Medicare Services (CMS) picked 25 organizations for the next stage of its AI challenge including IBM, Booz Allen Hamilton and Mayo Clinic. The organizations are competing for a $1 million prize to develop the best tool for predicting patient health outcomes. CMS says the AI challenge, which launched in March, will accelerate the development of AI solutions that aid clinicians in predicting health outcomes and keeping patients healthy. The central goal is to develop AI-driven predictions healthcare providers and clinicians participating in CMS Innovation Center models can use, CMS officials said. RELATED: CMS offers up to $1.6M in AI challenge for better healthcare prediction tools The challenge was created in partnership with the American Academy of Family Physicians and the Laura and John Arnold Foundation.