Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.

Area of Science:

• Medical Informatics
• Computational Biology
• Public Health

Background:

• Obesity is a complex global health issue requiring innovative management strategies.
• Traditional approaches to obesity management have limitations in personalization and prediction.
• Artificial intelligence (AI) offers novel tools for analyzing complex health data.

Purpose of the Study:

• To review the clinical and research applications of AI, ML, and DL in obesity.
• To assess AI's role in identifying risk factors, predicting outcomes, and personalizing obesity treatments.
• To evaluate AI's potential to enhance healthcare interventions for obesity.

Main Methods:

• Comprehensive literature search of PubMed and Google Scholar.
• Inclusion of studies on AI in obesity research, management, and therapeutic interventions.
• Review of observational studies, systematic reviews, and clinical applications.

Main Results:

• AI models, including ML and DL, are utilized for obesity prediction, patient stratification, and personalized management.
• AI facilitates risk prediction, early detection, and individualized treatment plans using diverse data.
• Model effectiveness varies based on data, goals, and interpretability; performance metrics guide selection.

Conclusions:

• AI presents significant potential for advancing personalized and efficient obesity care.
• Challenges including data quality, ethics, and technical requirements must be addressed.
• Harnessing AI is crucial for precision healthcare in obesity research and treatment.