AIRE (AI-ECG Risk Estimation)

Multi-outcome AI-ECG risk-estimation platform that turns a single 12-lead ECG into a patient-specific survival curve, using a residual convolutional neural network trained with a discrete-time survival loss to predict not just risk but time-to-event. Beyond all-cause and cardiovascular mortality, separately fine-tuned heads predict future ventricular arrhythmia, complete heart block, atrial fibrillation, atherosclerotic cardiovascular disease, heart failure, and (in follow-up work) hypertension -- all from a single resting ECG, including in ECGs a cardiologist would read as normal. Derived on 1.16 million ECGs from 189,539 Beth Israel Deaconess Medical Center patients and externally validated across transnational cohorts in the USA, Brazil (CODE), and the UK (UK Biobank). A variational autoencoder and genome/phenome-wide association analyses were used to show the model's predictions track biologically plausible features (QRS morphology, LV structure/function, and loci linked to cardiac structure, QT interval, and biological aging). NHS trials of AIRE were planned for late 2025. No public code or model weights have been released.