ReviewJournal of family medicine and primary care2023
Applications of artificial intelligence in the management of childhood obesity.
Review in Journal of family medicine and primary care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Testing an adapted obesity prevention intervention in under resourced schools: a pilot clustered randomized controlled trial.Scientific reports · 2025Trial
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- International consensus on adolescent metabolic health: prevention of obesity and type 2 diabetes.Diabetology & metabolic syndrome · 2026Review
- Humanizing the pediatric intensive care unit through new technologies and artificial intelligence.Frontiers in health services · 2026Article
- Novel Therapeutic Strategies for Obesity-Related Glomerulopathy.Current hypertension reports · 2025Review
- Integrating digital health into pediatric obesity management: Current practices and future perspectives.Obesity pillars · 2025Review
- Point of Care Testing, Rapid Next Generation Sequencing and Artificial Intelligence in Pediatric and Neonatal Healthcare: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Comprehending the Epidemiology and Aetiology of Childhood Obesity: Integrating Life Course Approaches for Prevention and Intervention.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Review
- Emerging Trends in Telehealth and AI-Driven Approaches for Obesity Management: A New Perspective.The Malaysian journal of medical sciences : MJMS · 2025Review
- Integrating Genetic Insights, Technological Advancements, Screening, and Personalized Pharmacological Interventions in Childhood Obesity.Advances in therapy · 2025Review
- Obesity classification: a comparative study of machine learning models excluding weight and height data.Revista da Associacao Medica Brasileira (1992) · 2025Article
- Childhood obesity in South Asian population.Obesity pillars · 2024Article
- Study of obesity research using machine learning methods: A bibliometric and visualization analysis from 2004 to 2023.Medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Childhood obesity has emerged as a significant public health challenge, with long-term implications that often extend into adulthood, increasing the susceptibility to chronic health conditions. Objective: The objective of this review is to elucidate the applications of artificial intelligence (AI) in the prevention and treatment of pediatric obesity, emphasizing its potential to complement and enhance traditional management methods. Methods: We undertook a comprehensive examination of existing literature to understand the integration of machine learning and other AI techniques in childhood obesity management strategies. Results: The findings from numerous studies suggest a strong endorsement for AI's role in addressing childhood obesity. Particularly, machine learning techniques have shown considerable efficacy in augmenting current therapeutic and preventive approaches. Conclusion: The intersection of AI with conventional obesity management practices presents a novel and promising approach to fortify interventions targeting pediatric obesity. This review accentuates the transformative capacity of AI, thereby advocating for continued research and innovation in this rapidly evolving domain.
Indexed as
Identifiers
What OpenQuestion holds
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.