Artificial intelligence-enabled electrocardiogram interpretation has the potential to enhance cardiovascular diagnostics and clinical outcomes, though challenges in generalizability and bias remain.
This review highlights the transformative potential of AI in ECG interpretation for cardiovascular diagnosis, while emphasizing the need for rigorous validation and ethical deployment.
The electrocardiogram (ECG) 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 integrative review synthesizes recent advances in AI-enabled ECG, 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 tackle critical challenges regarding generalizability, algorithmic bias, privacy, and regulatory systems. Finally, we outline research priorities, including curated open datasets, and deployment in resource-constrained settings. With stringent validation, transparent governance, and human-centered design, AI-ECG has the potential to enhance cardiovascular diagnostics and clinical outcomes across a variety of healthcare settings.
Lobodzinski et al. (Fri,) conducted a review in Cardiovascular disease. Artificial intelligence-enabled ECG was evaluated. Artificial intelligence-enabled electrocardiogram interpretation has the potential to enhance cardiovascular diagnostics and clinical outcomes, though challenges in generalizability and bias remain.