Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review

Artificial intelligence-enabled electrocardiography (AI-ECG) has rapidly advanced from experimental models to clinically deployed tools. This review outlines the evolution of AI-ECG across key domains including arrhythmia detection, structural heart disease diagnosis, and digital biomarker development. We summarize recent evidence from pragmatic randomized trials and prospective cohort studies that demonstrate the real-world utility of this approach across diverse populations and care settings. AI-ECG has demonstrated consistent accuracy in identifying conditions such as left ventricular systolic dysfunction, hypertrophic cardiomyopathy, and atrial fibrillation, with some studies reporting improved diagnostic rates, earlier intervention, and selected settings, reduced mortality. In addition to diagnostic support, AI-ECG enables longitudinal risk monitoring and screening for systemic diseases. Despite these advances, challenges remain around model generalizability, workflow integration, and regulatory adaptation. This review highlights both the clinical promise and the implementation hurdles of AI-ECG, underscoring the need for rigorous validation and thoughtful deployment to ensure its safe and effective integration into routine care.