Artificial Intelligence in Obesity Prevention

Area of Science:

• Medical Informatics
• Public Health
• Artificial Intelligence in Medicine

Background:

• Obesity is a complex disorder linked to chronic diseases like cancer, diabetes, and cardiovascular disease.
• Early identification and diagnosis of obesity are critical for managing associated health risks.
• Traditional anthropometric measures are being complemented by advanced Artificial Intelligence (AI) prediction models.

Purpose of the Study:

• To conduct a comprehensive analysis of research on the impact of AI in obesity prevention.
• To explore AI's role in assessing and predicting obesity.
• To identify trends and guide future AI applications in obesity research.

Main Methods:

• A scoping study was performed using AI to analyze research databases (PubMed, Scopus, Web of Science, Google Scholar).
• Data from February 2009 to July 2025 were compiled and analyzed.
• Employed AI approaches were arranged to identify patterns and trends in obesity prediction.

Main Results:

• AI models can identify significant obesity patterns and links between factors and weight outcomes.
• Deep learning and machine learning (logistic regression, decision trees, neural networks) provide new insights for obesity prediction.
• Chatbots can serve as valuable resources for clinical knowledge and policy development in obesity medicine.

Conclusions:

• AI presents promising solutions for modern challenges in obesity prediction and detection.
• Further research is needed to fully leverage AI's potential in obesity management.
• AI enhances the understanding and application of data analytics in obesity research.