{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:02:42Z","timestamp":1792036962312,"version":"4.4.0"},"reference-count":79,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care.<\/jats:p>","DOI":"10.3390\/bioengineering12121320","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:02:48Z","timestamp":1764774168000},"page":"1320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Integration of Artificial Intelligence and Wearable Devices in Pediatric Clinical Care: A Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Huili","family":"Zheng","sequence":"first","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"},{"name":"The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2750-0147","authenticated-orcid":false,"given":"Pragya","family":"Sharma","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"},{"name":"The Center for AI in Children\u2019s Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7056-2824","authenticated-orcid":false,"given":"Matthew","family":"Johnson","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"},{"name":"The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5178-9182","authenticated-orcid":false,"given":"Matteo","family":"Danieletto","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"},{"name":"The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3523-693X","authenticated-orcid":false,"given":"Eugenia","family":"Alleva","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA"},{"name":"The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, 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USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chethan","family":"Sarabu","sequence":"additional","affiliation":[{"name":"Health Tech Hub, Cornell Tech, Weill Cornell Medicine, New York, NY 10044, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-0336","authenticated-orcid":false,"given":"Bjoern M.","family":"Eskofier","sequence":"additional","affiliation":[{"name":"Institute of AI in Medicine, LMU Munich & LMU Hospital, 85152 Munich, Germany"},{"name":"Institute of AI for Health, Helmholtz Munich, 85764 Neuherberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuri","family":"Ahuja","sequence":"additional","affiliation":[{"name":"Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, 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