{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T14:08:29Z","timestamp":1789567709868,"version":"build-2803163510"},"reference-count":41,"publisher":"Brieflands","issue":"1","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["brieflands.com"],"crossmark-restriction":false},"short-container-title":["Int J Endocrinol Metab"],"abstract":"<jats:p>Context: Managing diabetes in children is challenging and requires continuous monitoring and structured self-care support. Artificial intelligence (AI)-driven digital tools may enhance self-management and improve health outcomes. This scoping review aimed to synthesize the existing evidence on AI-driven digital tools for pediatric diabetes self-management, focusing on their functionalities, benefits, and limitations. Evidence Acquisition: From an initial pool of 97 articles published between January 2010 and March 1, 2025, 19 studies met the predefined inclusion criteria. A systematic search was conducted in PubMed, Scopus, Web of Science, CINAHL, PsycINFO, and Embase using predefined keywords. Two independent reviewers performed screening and data extraction. Methodological quality was appraised using the Cochrane Risk of Bias Tool, the Newcastle-Ottawa Scale, and the Critical Appraisal Skills Programme (CASP) Checklist. Qualitative and quantitative findings were synthesized thematically. Results: The 19 included studies addressed several overlapping application domains of AI-driven digital tools for pediatric diabetes self-management. Overall, 8 studies focused primarily on glucose prediction, glycemic monitoring, or pattern recognition; 4 studies evaluated insulin dosing optimization and clinical decision-support systems; 6 studies investigated personalized education, behavioral support, user engagement, or self-management learning interventions; 5 studies examined telemedicine, remote monitoring, or healthcare access; and 5 studies primarily addressed ethical, methodological, transparency, or implementation challenges. Discussion: AI-based digital tools have considerable potential to support diabetes self-management in children. Future research should prioritize long-term evaluations, the inclusion of diverse populations, and rigorous assessments of clinical outcomes, data privacy, and algorithm performance.<\/jats:p>","DOI":"10.5812\/ijem-169084","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:35:56Z","timestamp":1789565756000},"update-policy":"https:\/\/doi.org\/10.5812\/crossmark_update_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Driven Digital Tools for Diabetes Self-management in Children: A Scoping Review"],"prefix":"10.5812","volume":"25","author":[{"given":"Maryam","family":"Nakhaee Moghadam","sequence":"first","affiliation":[{"name":"Children and Adolescents Health Research Center, Resistant Tuberculosis Institute, Zahedan University of Medical Sciences and Health Services, Zahedan, 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