Advances in Interpretation of Electrocardiogram by Artificial Intelligence

The 12-lead electrocardiogram is essential for cardiovascular diagnosis but limited by inter-observer variability, low sensitivity for subclinical disease, and labor-intensive telemonitoring analysis. Artificial intelligence (AI), particularly deep learning, addresses these constraints by extracting high-dimensional patterns that correlate with arrhythmias, structural abnormalities, and systemic conditions. This review synthesizes recent AI-enabled ECG advances, covering technical foundations—including foundation models and validation strategies—and clinical applications such as arrhythmia detection, structural heart disease identification, and digital biomarker derivation. We discuss emerging trends like self-supervised learning, multimodal integration, generative models, and explainability techniques. Furthermore, we address critical challenges regarding generalizability, algorithmic bias, privacy, and regulatory frameworks. Finally, we outline research priorities, including curated open datasets, personalized continuous-learning systems, and deployment in resource-limited settings. With rigorous validation, transparent governance, and human-centered design, AI-ECG has the potential to democratize cardiovascular diagnostics and improve clinical outcomes across diverse environments.