Structured Graphical Abstract.

An ECG-AI model trained at the MHI (a tertiary cardiac centre) predicts 5-year incident atrial fibrillation or flutter (AF) in an internal independent test data set (MHI; AUC-ROC .78) and an external population (MIMIC-IV; AUC-ROC .77). The ECG-AI outperforms existing clinical (CHARGE-AF) and polygenic scores (PGS). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P < .001), with minimal changes to the AUC-ROC (.76–.77). Created with Biorender.com. HR, hazard ratio; MIMIC-IV, Medical Information Mart for Intensive Care-IV; AUC-ROC, area under the receiver operating characteristic curve.