Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?
Abstract
Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy and adaptability, especially in complex clinical settings. In contrast, AI-driven models, particularly those employing machine learning and deep learning architectures, have demonstrated improved diagnostic performance across a broad spectrum of cardiovascular diseases, including atrial fibrillation, acute myocardial infarction, hypertrophic cardiomyopathy, and valvular heart disease. Notably, AI-ECG is now able to detect subclinical ventricular dysfunction, stratify long-term risk, and anticipate major adverse events before overt clinical manifestations occur. In addition to diagnosis, AI-ECG is emerging as a decision support tool in scenarios characterized by diagnostic uncertainty, such as syncope and cardio-oncology, and may significantly optimize triage and resource allocation. Multiparametric approaches further extend its utility, enabling simultaneous prediction of structural, functional, and electrical cardiac parameters. Wearable devices integrated with AI improve continuous monitoring and may decentralize arrhythmia detection and sudden cardiac death prevention. Despite these advances, critical challenges remain. Poorly explainable AI models, algorithmic bias, overfitting, data governance, and regulatory uncertainty…
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