{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T03:21:15Z","timestamp":1790133675416,"version":"4.0.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This review critically appraises the current evidence, methodological rigor, clinical readiness, and translational challenges of AI in pediatric arrhythmia detection, risk stratification, and management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Main findings<\/jats:title>\n                    <jats:p>While purpose-built DL models demonstrate strong diagnostic performance for specific electrical phenotypes\u2014such as Wolff-Parkinson-White (WPW) syndrome, long QT syndrome (LQTS), and neonatal bradycardia\u2014the 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1932796","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T14:09:20Z","timestamp":1785506960000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives"],"prefix":"10.3389","volume":"14","author":[{"given":"Hailin","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Pediatric Cardiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University","place":["Jinan, 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