Enhancing explainability in ECG analysis through evidence-based AI interpretability.

While pre-trained neural networks, e.g., for diagnosis from electrocardiograms (ECGs), are already available and show remarkable performance, their lack of transparency prevents translation to clinical practice.

Explainable AI associates ECG aging effects with increased cardiovascular risk in a longitudinal population study.

Aging affects the 12-lead electrocardiogram (ECG) and correlates with cardiovascular disease (CVD). AI-ECG models estimate aging effects as a novel biomarker but have only been evaluated on single ECGs-without utilizing longitudinal data. We validated an AI-ECG model, originally trained on Brazilian data, using a German cohort with over 20 years of follow-up, demonstrating similar performance (r = 0.