AI Smartwatch Detects Hidden Heart Disease with 88% Accuracy

In a groundbreaking development for cardiovascular health monitoring, researchers have successfully demonstrated that an artificial intelligence algorithm paired with single-lead electrocardiogram (ECG) sensors on common smartwatches can accurately detect structural heart diseases. The study, to be presented at the American Heart Association's Scientific Sessions 2025, represents the first prospective research showing AI can identify multiple structural heart conditions—including weakened pumping ability, damaged valves, and thickened heart muscle—using the same technology millions of people already wear daily. Led by Dr. Arya Aminorroaya of Yale New Haven Hospital and senior author Dr. Rohan Khera, director of the Cardiovascular Data Science Lab at Yale School of Medicine, the research team developed their AI model using an extensive database of over 266,000 ECG recordings from more than 110,000 patients, then validated it across multiple populations including community hospital patients and participants from Brazil's ELSA-Brasil study.

The real-world testing involved 600 participants who underwent 30-second single-lead ECGs using smartwatch sensors on the same day they received comprehensive heart ultrasound evaluations. The results were remarkably promising: the AI algorithm achieved 88% accuracy in detecting structural heart disease from smartwatch readings, maintaining high performance even when accounting for real-world signal interference. The technology demonstrated 86% sensitivity for identifying people with actual heart conditions and an impressive 99% negative predictive value for accurately ruling out disease. This breakthrough is particularly significant because structural heart diseases, unlike rhythm disorders such as atrial fibrillation, typically require specialized echocardiogram equipment that isn't widely available for routine screening. The researchers intentionally trained their model to handle signal "noise" similar to what occurs during real-world smartwatch use, making the technology robust and reliable for everyday applications.

While the study authors acknowledge limitations including a small number of actual disease cases in the prospective trial and some false positive results, they emphasize the transformative potential of making structural heart disease screening accessible through devices people already own. The research team plans to evaluate the AI tool in broader community settings and explore integration into heart disease screening programs. This innovation could revolutionize preventive cardiology by enabling early detection of serious heart conditions before they progress to cardiac events or serious complications, potentially saving lives through timely intervention. The study represents a significant step toward democratizing advanced cardiac screening and making sophisticated health monitoring available to the general population through consumer-grade wearable technology.