Artificial Intelligence Applications in Pediatric Primary Care for Weight Management: A Systematic Review

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.

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 5-19 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.

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.

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.