Artificial intelligence in electrocardiogram interpretation for cardiovascular diagnosis and risk prediction: a systematic review of evidence through May 2026
Artificial intelligence-enabled electrocardiogram (ECG) analysis has expanded across cardiovascular diagnosis, monitoring, and risk prediction, but published studies vary in methodology, validation, and clinical applicability. We systematically reviewed AI applications in ECG interpretation for cardiovascular diagnosis and risk prediction to define the evidence available through May 2026. This systematic review followed PRISMA 2020. PubMed and the Cochrane Library were searched from database inception through May 2026 for original human studies evaluating artificial intelligence, machine learning, or deep learning applications in ECG-based cardiovascular detection, classification, diagnosis, monitoring, or risk prediction. Risk of bias was assessed using PROBAST. Because of substantial heterogeneity in study populations, ECG modalities, model architectures, validation strategies, and reported outcomes, findings were synthesized narratively. A total of 108 studies were included. Arrhythmia detection, particularly atrial fibrillation (AF), was the most mature and frequently evaluated application domain. Benchmark-based studies commonly reported high apparent performance, whereas larger clinical cohorts and externally validated studies generally showed more moderate but more clinically credible estimates. The most common methodological limitations were concentrated in the analysis domain, particularly limited external validation, overfitting risk, unclear train-test separation, class imbalance, and incomplete reporting of calibration and robustness. AI-enabled ECG interpretation shows strongest support for arrhythmia detection and automated ECG classification, while structural disease screening and prognostic modeling remain promising but less mature. Future studies should prioritize prospective, multicenter, externally validated, and workflow-integrated designs with transparent reporting, calibration assessment, cost-effectiveness evaluation, and equitable assessment across diverse populations.