Each year in the U.S., more than 300,000 people die from sudden cardiac arrest, a condition where the heart’s electrical system malfunctions without warning. The medical emergency can kill both high-risk older adults and young athletes with no history of heart issues, and while internal defibrillators that shock the heart can save lives, figuring out who actually needs one remains a high-stakes guessing game. With a new tool that could transform how that game is played, UC Berkeley researchers have discovered a previously unrecognized signal in electrocardiograms that can better detect high-risk patients before their heart stops.

Full report : A UC Berkeley-led project trained an AI system on hundreds of thousands of EKGs. The resulting risk predictions are much better than existing methods, paving the way to save lives at scale.