Can AI predict heart failure hospitalization risk using patient-generated ecg data from smartwatches ?

Can consumer smartwatches provide ECG data precise enough to anticipate heart-failure hospitalizations? Real-time analysis of these wearable signals could warn clinicians before a patient’s condition worsens, but the reliability of such predictions hinges on the quality of the recordings and sustained user engagement.

Background

Heart failure patients frequently exhibit premonitory arrhythmias days before decompensation, creating a potential window for early intervention. Consumer-grade smartwatches can capture single-lead ECG traces, and multiple studies have evaluated whether deep-learning pipelines trained on these signals can forecast future heart-failure (HF) hospitalizations. Reported discrimination metrics for prototype models hover around 70 % when trained solely on device data, and have not surpassed traditional risk calculators that incorporate clinical variables and laboratory values (European Society of Cardiology Congress 2023, Late-Breaking Science presentation “Deep learning from smartwatch ECGs to predict heart-failure hospitalization: the WATCH-HF pilot,” May 12 2026). Research efforts have explored transformer-based architectures that convert raw watch ECGs into risk-score embeddings, yet these approaches remain unvalidated externally, lack regulatory clearance for routine use, and continue to be constrained by prevalent data-quality issues—motion artifacts, poor lead contact, and inter-device sampling-rate variability—undermining consistent model performance.