Introduction

Artificial intelligence (AI) has become a game-changer in the healthcare sector. Recent studies have shown promising evidence of AI-powered tools in decision support and digital health interventions for weight loss. However, no comprehensive review has been conducted to summarize the applications of AI algorithms, models, and methods in obesity research. This groundbreaking study aims to provide a methodological review of AI’s role in measuring, predicting, and treating childhood and adult obesity. The study highlights how AI is advancing obesity research. By analyzing and categorizing AI methodologies used in the obesity literature, researchers hope to identify synergies, patterns, and trends that can inform future scientific investigations. This comprehensive review serves as a valuable resource for researchers and healthcare professionals interested in leveraging AI techniques to tackle the global obesity epidemic.

AI in Diagnosis and Treatment

One of the key findings of the study is the significant role AI plays in the diagnosis and treatment of obesity. AI-powered tools can analyze vast amounts of patient data, including genetic information, medical history, and lifestyle factors, to provide personalized treatment plans. This individualized approach has the potential to improve patient outcomes and reduce healthcare costs by tailoring interventions to each patient’s unique needs.

Accessibility and Public Health Impact

A crucial aspect of this research is its emphasis on making AI-driven obesity research and treatment accessible to the public. By providing a beginner-friendly introduction to core AI methodologies, the study aims to facilitate the dissemination and adoption of AI techniques among healthcare professionals and the general public. This democratization of AI in healthcare has the potential to empower individuals to take control of their health and make informed decisions regarding obesity prevention and management.

Implications for the Future

The findings of this study have significant implications for the future of obesity research and treatment. By leveraging AI, healthcare professionals can gain valuable insights into the complex factors contributing to obesity and develop targeted interventions. Furthermore, the integration of AI in obesity research can pave the way for innovative approaches to prevention, early detection, and personalized treatment.

Conclusion

The application of AI in obesity research holds promise for improving patient outcomes and addressing the global obesity epidemic. This study provides a comprehensive review of AI methodologies used in obesity research, highlighting their potential to measure, predict, and treat childhood and adult obesity. By making this research accessible to both professionals and patients.