Multi-task 12-lead ECG model with output heads for incident atrial-fibrillation risk (as a survival curve), incident mortality risk, prevalent AF classification, sex classification, and age regression. Built on a 1D CNN over the raw waveform, and developed by the Broad Institute's ML4H group as a successor to their ECG-AI model published in Circulation. Trained on ECGs from UK Biobank and Massachusetts General Hospital, neither of which is publicly released.

Self-supervised ECG representation learned purely from patient identity: the model is trained so that ECGs from the same patient, recorded at different times, map to nearby points in latent space, with no other labels required. Linear models trained on these representations showed a 51% average performance gain over training from scratch across sex classification, age regression, LVH detection, and AF detection. Developed by the Broad Institute's ML4H group and trained on 3.2 million private ECGs from Massachusetts General Hospital; 12-lead, lead-I-only, and lead-II-only checkpoints are all released.