ReviewMedicina (Kaunas, Lithuania)2025
The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.
Review in Medicina (Kaunas, Lithuania), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Mobile and Web Apps for Weight Management in Overweight and Obese Adults: An Updated Umbrella Review and Meta-Analysis.International journal of environmental research and public health · 2025Pooled it
- Applying Artificial Intelligence to Childhood Obesity: T2DM and MASLD Risk Predictive Models.Diagnostics (Basel, Switzerland) · 2026Review
- Obesity Interpreter: A Digital Tool for Assessment of Childhood Obesity.Indian pediatrics · 2026Article
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence in public health-challenges and opportunities.European journal of clinical nutrition · 2026Review
- Artificial Intelligence-Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- Recalibrating Resting Energy Expenditure Prediction Equations in Asian Older Adults with Multimorbidity.Nutrients · 2026Article
- Intelligence-driven Mechanomedicine for Weight Rebound in Obesity.Current obesity reports · 2026Review
- Review
- Multimodal (Bio)Markers and Risk of Obesity - A Comprehensive Scoping Review.Advances in nutrition (Bethesda, Md.) · 2026Article
- A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.Frontiers in artificial intelligence · 2026Article
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Machine learning framework for predicting susceptibility to obesity.Scientific reports · 2025Article
- Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity.Food science & nutrition · 2025Review
- Personalized Nutrition in Pediatric Chronic Diseases.Metabolites · 2025Review
- Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis.Scientific reports · 2025Article
- Applications of Artificial Intelligence in Bariatric Surgery: Enhancing Education, Monitoring, and Outcomes.Obesity surgery · 2025Article
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necessary to improve risk prediction and management strategies. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in healthcare, offering novel approaches to chronic disease prevention. This narrative review explores the role of AI/ML in obesity risk prediction and management, with a special focus on childhood obesity. We begin by examining the multifactorial nature of obesity, including genetic, behavioral, and environmental factors, and the limitations of traditional approaches to predict and treat morbidity associated obesity. Next, we analyze AI/ML techniques commonly used to predict obesity risk, particularly in minimizing childhood obesity risk. We shift to the application of AI/ML in obesity management, comparing perspectives from healthcare providers versus patients. From the provider's perspective, AI/ML tools offer real-time data from electronic medical records, wearables, and health apps to stratify patient risk, customize treatment plans, and enhance clinical decision making. From the patient's perspective, AI/ML-driven interventions offer personalized coaching and improve long-term engagement in health management. Finally, we address key limitations and challenges, such as the role of social determinants of health, in embracing the role of AI/ML in obesity management, while offering our recommendations based on our literature review.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.