{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:44:30Z","timestamp":1792075470868,"version":"4.4.0"},"reference-count":97,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Childhood Obesity"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Pediatric overweight and obesity (OW\/OB) is a major public health concern associated with significant short- and long-term health risks. Primary care is a key setting for early identification and management, yet current approaches face barriers related to implementation, workflow, and family engagement. This systematic review examined how artificial intelligence (AI) has been used in pediatric primary care and closely connected clinical settings to support OW\/OB management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We conducted a systematic review registered in PROSPERO (CRD420251103662) and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. MEDLINE, Embase, CINAHL Ultimate, Compendex, and CENTRAL were searched for English-language studies published from 2007 to July 9, 2025. Eligible studies included children and adolescents ages 5\u201319 years with OW\/OB, were conducted in or linked to primary care, and included at least one AI-based approach. Dual-reviewer screening, structured data extraction, and methodological appraisal using the Mixed Methods Appraisal Tool were completed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Of 3854 records identified, 20 studies met inclusion criteria. Most studies (80%) targeted patient families, while 20% included both families and clinicians. Machine learning was the most common AI approach (60%) and was primarily used for obesity risk prediction or stratification. Fewer studies examined family engagement, counseling, documentation review, or generative AI tools. Methodological quality ranged from moderate to high.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion:<\/jats:title>\n                    <jats:p>AI tools in pediatric primary care primarily support early identification of OW\/OB risk, but evidence on workflow integration, sustained engagement, and long-term clinical impact remains limited. Future research should prioritize prospective, workflow-embedded studies, meaningful clinical outcomes, equity, privacy, governance, clinician training, and policy oversight.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21532176261498254","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T07:35:24Z","timestamp":1791876924000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Applications in Pediatric Primary Care for Weight Management: A Systematic Review"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2758-0487","authenticated-orcid":false,"given":"Nicholas","family":"Cook","sequence":"first","affiliation":[{"name":"School of Public Health, Texas A&amp;M University, College Station, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anindita","family":"Roy","sequence":"additional","affiliation":[{"name":"School of Public Health, Texas A&amp;M University, College Station, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margret J.","family":"Foster","sequence":"additional","affiliation":[{"name":"Center for Systematic Reviews &amp; Research Syntheses, Houston, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle M.","family":"Holland","sequence":"additional","affiliation":[{"name":"Center for Systematic Reviews &amp; Research Syntheses, Houston, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3430-6860","authenticated-orcid":false,"given":"Alfiya Shaikh","family":"Mohd Rafiq","sequence":"additional","affiliation":[{"name":"School of Public Health, Texas A&amp;M University, College Station, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Jungbauer","sequence":"additional","affiliation":[{"name":"Pacific Northwest Evidence-based Practice Center, Oregon Health &amp; Science University, Portland, Oregon, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Yudkin","sequence":"additional","affiliation":[{"name":"School of Public Health, Texas A&amp;M University, College Station, Texas, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nepr.2008.07.006"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.11124\/JBISRIR-2017-003695"},{"key":"e_1_3_2_4_1","unstructured":"3.World Health Organization. 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J Nurs Care Qual 2025;40(3):193\u2013201.","journal-title":"J Nurs Care Qual"},{"key":"e_1_3_2_83_1","doi-asserted-by":"crossref","unstructured":"82.Monta\u00f1ez CCA Fergus P Curbelo Monta\u00f1ez AC et al. Deep learning classification of polygenic obesity using genome wide association study SNPs. In: 2018 International Joint Conference on Neural Networks (IJCNN) Rio de Janeiro Brazil. IEEE: Piscataway NJ pp. 1\u20138.","DOI":"10.1109\/IJCNN.2018.8489048"},{"key":"e_1_3_2_84_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0143003"},{"key":"e_1_3_2_85_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.landig.2026.100980"},{"issue":"5","key":"e_1_3_2_86_1","doi-asserted-by":"crossref","first-page":"101006","DOI":"10.1016\/j.landig.2026.101006","article-title":"Adolescent obesity in the digital age: Navigating risks and opportunities","volume":"8","author":"Partridge SR","year":"2026","unstructured":"85.Partridge SR, , Todd AR, , Redfern J, et al. Adolescent obesity in the digital age: Navigating risks and opportunities. Lancet Digit Health 2026;8(5):101006.","journal-title":"Lancet Digit Health"},{"key":"e_1_3_2_87_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.obpill.2023.100065"},{"key":"e_1_3_2_88_1","unstructured":"87.Obesity Medicine Association. Obesity Algorithm\u00ae. 2026. Available from: https:\/\/obesitymedicine.org\/resources\/obesity-algorithm\/ [Last accessed: September1 2026]."},{"key":"e_1_3_2_89_1","unstructured":"88.U.S. Food and Drug Administration. Device software functions including mobile medical applications. 2022. Available from: https:\/\/www.fda.gov\/medical-devices\/digital-health-center-excellence\/device-software-functions-including-mobile-medical-applications [Last accessed: May3 2026]."},{"key":"e_1_3_2_90_1","unstructured":"89.American Medical Association. Commercial payer coverage for digital medicine codes. 2023. Available from: https:\/\/www.ama-assn.org\/system\/files\/issue-brief-commercial-payer-coverage-digital-care.pdf [Last accessed: May3 2026]."},{"key":"e_1_3_2_91_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aax2342"},{"key":"e_1_3_2_92_1","doi-asserted-by":"publisher","DOI":"10.1186\/1748-5908-4-50"},{"key":"e_1_3_2_93_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1553-7250(08)34030-6"},{"key":"e_1_3_2_94_1","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.89.9.1322"},{"key":"e_1_3_2_95_1","doi-asserted-by":"publisher","DOI":"10.1177\/15305627261426075"},{"key":"e_1_3_2_96_1","unstructured":"95.Rios F Praas R Balland PA. The growing number of non-English models. AI World 2025. Available from: https:\/\/aiworld.eu\/story\/the-growing-number-of-non-english-models [Last accessed: May3 2026]."},{"key":"e_1_3_2_97_1","unstructured":"96.Wagner CS. China\u2019s historic rise to the top of the scientific ladder. Quincy Institute for Responsible Statecraft 2025. Available from: https:\/\/quincyinst.org\/research\/chinas-historic-rise-to-the-top-of-the-scientific-ladder\/ [Last accessed: May3 2026]."},{"key":"e_1_3_2_98_1","unstructured":"97.Mosheim T. China produces more AI research than US UK and EU combined. Times Higher Education 2025. 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