Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives

Background The interpretation of pediatric electrocardiograms (ECGs) and management of childhood arrhythmias represent specialized clinical disciplines complicated by age-dependent physiological evolution. While artificial intelligence (AI) has transformed adult cardiology, its application to pediatric electrophysiology remains largely in the research phase. Objective This review critically appraises the current evidence, methodological rigor, clinical readiness, and translational challenges of AI in pediatric arrhythmia detection, risk stratification, and management. Methods A synthesis of contemporary literature was conducted, evaluating machine learning (ML), deep learning (DL), large language models (LLMs), wearable sensors, and intensive care monitoring across pediatric cohorts. Main findings While purpose-built DL models demonstrate strong diagnostic performance for specific electrical phenotypes—such as Wolff-Parkinson-White (WPW) syndrome, long QT syndrome (LQTS), and neonatal bradycardia—the vast majority of published tools remain unvalidated retrospective proofs-of-concept. Current AI algorithms analyze isolated ECG waveforms under curated conditions and cannot replace holistic clinical evaluations incorporating patient history, family screening, genetics, and multi-modality diagnostic testing. Significant barriers persist, including pervasive data scarcity, lack of prospective external validation, limited saliency map reproducibility in Explainable AI (XAI), and uncalibrated false alarms. Furthermore, adult-trained algorithms and general-purpose LLMs yield unacceptable diagnostic error rates when applied to children. Conclusions AI holds promise for enhancing pediatric arrhythmia care, but clinical integration requires moving beyond isolated performance metrics. Future progress hinges on prospective multicenter validation, privacy-preserving federated learning, multimodal data integration, and explicit definition of AI's role as a clinical decision-support tool within real-world workflows.