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Obesity01:24

Obesity

The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in adipocytes...

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Control of Eating Behavior Using a Novel Feedback System
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Published on: May 8, 2018

Artificial Intelligence for Weight Management in Children: A Narrative Review.

Valeria Calcaterra1,2, Luca Marin3,4, Hellas Cena5,6

  • 1Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy.

Healthcare (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise for enhancing pediatric weight management through personalized monitoring and risk stratification. However, current evidence is primarily predictive, necessitating further interventional research for clinical application in childhood obesity.

Keywords:
artificial intelligencechildrennutritionobesityphysical activityweight management

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Published on: May 8, 2018

Multidisciplinary Approach to Obesity Management: A Case Report
05:10

Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

Area of Science:

  • Public Health
  • Medical Informatics
  • Pediatrics

Background:

  • Childhood overweight and obesity are increasing globally, posing significant health risks.
  • Current pediatric weight management strategies yield variable results, indicating a need for advanced approaches.
  • Artificial intelligence (AI) offers potential for improved prevention and management of pediatric obesity.

Purpose of the Study:

  • To review the applications of AI in pediatric weight management.
  • To identify key domains and methodologies of AI use in this field.
  • To assess the current evidence base and future research directions.

Main Methods:

  • A narrative review of studies published up to January 2026 was conducted.
  • Searches were performed on PubMed, Scopus, and Web of Science using terms related to AI and pediatric weight management.
  • 51 studies were included after screening, synthesizing findings across application domains.

Main Results:

  • AI applications in pediatric weight management span risk assessment, dietary support, physical activity monitoring, behavioral support, and clinical decision support.
  • AI is frequently used for predictive modeling based on various individual and family factors.
  • The existing evidence is predominantly observational and predictive, with limited interventional and real-world implementation studies.

Conclusions:

  • AI can serve as a valuable tool in multidisciplinary pediatric weight management, especially for early risk identification and personalized support.
  • Current AI applications are largely exploratory and predictive, not yet demonstrating definitive intervention effectiveness.
  • Further research is essential to validate AI's effectiveness, safety, and equitable implementation in pediatric weight management.