The electrocardiogram and artificial intelligence: turning signals into insights in pediatric and congenital heart disease

Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate that deep learning models can accurately identify arrhythmias, ventricular dysfunction, and CHD from standard ECG. Convolutional neural networks remain the dominant architecture, although transformer-based foundation models and self-supervised learning approaches are increasingly being explored. AI-ECG applications in PCHD have expanded from automated interpretation toward proactive risk stratification, including prediction of ventricular dysfunction, mortality, and sudden cardiac death risk. Additional work has investigated wearable monitoring, telemetry analysis, and integration with longitudinal clinical data. Despite promising performance, most studies remain retrospective and single-center, with limited external validation and challenges related to small datasets, physiologic heterogeneity, and age-dependent ECG variation. Summary AI-ECG has the potential to transform PCHD by improving diagnostic accuracy, enabling earlier disease detection, and enhancing longitudinal risk assessment. Broader clinical implementation will require multicenter collaboration, prospective validation, standardized datasets, and careful attention to ethical, regulatory, and equity considerations.