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.