Electrocardiograms (ECGs) remain a foundational pillar of cardiovascular diagnostics, providing rapid, non-invasive diagnosis and being universally accessible to all clinicians. An ECG captures the electrical signals of the heart via a standard 12-lead configuration, offering insights into arrhythmias, conduction delays, ischemic injury, structural remodeling, and systemic pathologies with cardiac implications. This review presents a structured framework for ECG interpretation by discussing general approaches to rate, rhythm, axis, intervals, and repolarization dynamics, and by outlining both cardiac and non-cardiac conditions associated with ECG abnormalities. We explore the accelerating pace of innovations in artificial intelligence (AI) for ECG analysis. Deep learning algorithms now rival and, in select domains, surpass expert clinicians in detecting left ventricular systolic and diastolic dysfunction, hypertrophic obstructive cardiomyopathy, and acute myocardial infarction. The integration of AI-enhanced ECG interpretation enables earlier disease recognition, refined risk stratification, and optimized clinical decision-making across acute and chronic care settings. This review systematically guides readers through ECG interpretation, linking fundamental principles with nuanced clinical patterns using AI to enhance accurate diagnosis and improve patient outcomes across a wide range of cardiovascular conditions.
Namjouyan et al. (2025) studied this question. AI-enhanced ECG analysis surpasses expert clinicians in detecting heart conditions, enabling earlier disease recognition and improved clinical decision-making.