Artificial Intelligence in Obesity

Artificial Intelligence in Obesity Abstract Background: One of the primary disorders that causes further health issues and is linked to several chronic illnesses, such as cancer, diabetes, metabolic syndrome, and cardiovascular diseases, is overweight and obesity. Therefore, it is critical to identify and diagnose obesity as soon as possible. In the field of obesity research, artificial intelligence (AI), which is categorized by machine learning (ML) and deep learning (DL), has become an essential tool. We provide a thorough analysis of the research on the effect of AI on the diagnosis, prevention, and treatment of obesity. The predominant features and results of the research contribute to our understanding in order to create clever and efficient therapies for predicting and treating obesity. Methodology: We performed a scoping study using AI to assess, predict, and treat obesity in PubMed, Scopus, Web of Science, Google Chrome, and chatbots. To find connections, patterns, and trends that could guide further research and the application of machine learning algorithms for advanced data analytics, we compiled and arranged the employed AI approaches. Findings: Artificial intelligence (AI) is a nonhuman entity's technological acquisition of knowledge and skill. AI operates to varied degrees independently of direct human supervision and generates adaptive output tasks depending on learnings from data input. Clinical professionals in obesity medicine may find chatbots valuable as a source of clinical and scientific knowledge, creating standard operating procedures, policies and procedures. AI could help precision medicine by supporting clinicians and facilitating interactive programming related to body composition imaging analyses, behavior coaching, personalized dietary intervention and physical activity recommendations, educational programming, predictive modeling to identify patients at risk for obesity related complications. AI may assist in finding trends in datasets about a medical facility or practice that might be utilized to evaluate value based care delivery and population health. According to the findings, AI models can be used to identify clinically significant obesity patterns or the connections between particular factors and weight outcomes. Conclusion: In summary, this work aims to contribute to a better understanding of obesity, including its detection, investigation, and treatment. Although the data gathered is encouraging, more study is needed to comprehend this illness fully.