AI in Clinical Medicine, ISSN 2819-7437 online, Open Access
Article copyright, the authors; Journal compilation copyright, AI Clin Med and Elmer Press Inc
Journal website https://aicm.elmerpub.com

Review

Volume 2, April 2026, e18

Artificial Intelligence Applications in Pediatric Obesity Medicine: Bridging Predictive Analytics, Clinical Decision Support, and Family-Centered Care

Figures

↓  Figure 1. AI workflow in pediatric obesity care pathway. The figure depicts the flow of various information types (e.g. medical records, wearable device data, family context, and environmental factors) into a single AI engine. This engine analyzes the aggregated data and provides insights and guidance to families, patients, and pediatric care teams. Actions taken by these groups generate new data, which are subsequently reintegrated into the AI engine, establishing a continuous feedback loop. Source: Figure created by authors JP, MS, and VSC using Microsoft PowerPoint from data extracted from sources [16–20]. AI: artificial intelligence.
Figure 1.
↓  Figure 2. Future integration model for AI in precision pediatric obesity medicine. This model illustrates a continuous learning system that integrates multi-omic, clinical, behavioral, environmental, and social determinants data to inform individualized obesity management. Following data intake and contextual integration, AI processing supports risk modeling, growth trajectory analysis, and phenotype clustering to guide precision phenotyping and treatment optimization. NLP-enabled documentation facilitates case identification and reduces clinical documentation burden. Real-time monitoring enables adaptive intervention adjustments and ongoing model refinement. Clinical decisions remain physician-led, with AI providing structured insights to support personalized treatment selection and longitudinal response assessment. Source: Figure created by authors JP, MS, and VSC using Microsoft PowerPoint from data extracted from sources [28–30]. AI: artificial intelligence; NLP: natural language processing.
Figure 2.

Tables

↓  Table 1. Current Artificial Intelligence Applications in Pediatric Obesity Medicine by Domain and Technology Category
 
AI domainCurrent applicationsImplementation status
This table summarizes key AI applications across predictive analytics, clinical decision support, remote monitoring, and natural language processing domains, including representative studies, technology approaches, reported performance metrics, and implementation status. Source: Table synthesized by authors JP, MS, and VSC from data extracted from [11, 12, 16–20]. Literature coverage: 2015–2024. AI: artificial intelligence.
Predictive analytics stageEarly obesity-risk prediction, analysis of growth trajectoriesResearch, early pilots, e.g., Esteva et al [12] demonstrated deep learning approaches; Ward et al [11] modeled childhood-to-adult obesity trajectories.
Clinical decision support phase (testing)Treatment planning assistance, guideline-support promptsEarly pilots, limited clinical testing, e.g., Barlow et al [17] expert committee framework; Sutton et al [16] CDSS overview
Remote monitoring (apps)Wearable analytics, diet trackingGrowing use, commercially available, e.g., Nahum-Shani et al [18] JITAI framework; Boushey et al [19] image-based dietary assessment.
Natural language processing adoption (studies)Identification of obesity cases, documentation aidEarly pilots, e.g., Kreimeyer et al [20] NLP systems for clinical information extraction.

 

↓  Table 2. Implementation Barriers and Proposed Solutions for AI Integration in Pediatric Obesity Care
 
ChallengeKey impactSolutions
This table categorizes major challenges to AI implementation, including technical, clinical, ethical, and equity-related barriers, along with evidence-based strategies and best practices for addressing each challenge category. Source: Table synthesized by authors JP, MS, and VSC from data extracted from [21–23]. AI: artificial intelligence; EHR: electronic health record.
Algorithmic bias (datasets and testing)Inaccurate predictions for minorities/underserved groupsUse diverse datasets, fairness/bias testing
Data privacy (learning)Compliance issues, trust concernsStrong encryption, avoid sharing raw data
Interpretability (AI interfaces)Reduced provider confidence, slower adoption due to “black box” outputsUse explainable AI models, improve interface clarity
Workflow Integration (EHR integration)Tools can disrupt workflow → increased clinical workUser-centered design, better operability with EHR platforms
Health equity (programs and adaptation)Digital divide, risk of widening existing disparitiesExpand technology access, culturally responsive AI design