Sudden cardiac death remains difficult to predict. The current clinical standard — reduced left ventricular ejection fraction (LVEF) — misses most at-risk patients and leads to many unnecessary defibrillator implants.

Researchers used deep learning on a large Swedish dataset of routine ECGs linked to death records and discovered a previously unknown ECG biomarker: a subtle slurred terminal R wave in lead aVL. This pattern is associated with higher risk of sudden cardiac death, likely linked to myocardial fibrosis.

Google-led multi-agent AI system that acts like a full data-science team. It processes 9k+ participants and millions of hours of wearable signals (HR, sleep, steps, etc.) to discover clinically useful biomarkers.

The Iterative Loop (core engine): Profile data + ground in literature, Parallel statistical + ML exploration, Iterative refinement — GapChecker & Critic agents repeatedly test, critique, and strengthen candidates, Adversarial validation (Critic vs Defender).