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Multidisciplinary Approach to Obesity Management: A Case Report

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Published on: May 30, 2025

Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.

Hyeseung Lee^1,2, Jiyoung Hwang^1,2, Dong Keon Yon^1,2,3,4,5

¹Department of Medicine, College of Medicine, Kyung Hee University, Seoul, Korea.

Journal of Obesity & Metabolic Syndrome

|September 9, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) offers personalized, data-driven strategies for obesity management, including risk prediction and digital therapeutics. AI-powered weight loss programs show significant results but require addressing privacy and equity concerns for broader public health impact.

Keywords:

Artificial intelligence Delivery of health care Machine learning Obesity Obesity management

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Area of Science:

Obesity research
Artificial intelligence in healthcare
Digital health

Background:

Global obesity prevalence is rising, posing a complex public health challenge.
Multidimensional treatment approaches are essential for effective obesity management.
Artificial intelligence (AI) advancements enable novel data integration for personalized strategies.

Purpose of the Study:

To explore the role of AI in personalized obesity management.
To review AI applications in weight loss and behavioral modification.
To identify challenges and future directions for AI in obesity care.

Main Methods:

Review of AI applications in obesity management, including risk prediction, clinical decision support, large language models, and digital therapeutics.
Analysis of AI-driven weight loss programs and their impact on behavioral changes.
Examination of ethical considerations and implementation challenges.

Main Results:

AI facilitates personalized, data-driven obesity management strategies.
AI-based weight loss programs demonstrate significant weight reduction and behavioral improvements.
AI enhances accessibility and provides continuous personalized feedback.

Conclusions:

AI holds significant potential for transforming obesity management through personalized interventions.
Addressing data privacy, security, transparency, and health equity is crucial for responsible AI implementation.
Long-term clinical trials and cost-effectiveness evaluations are needed to optimize AI's public health impact in obesity care.