Core Viewpoint - The article emphasizes the importance of fat distribution over total fat amount in determining health risks, highlighting a new AI-based research approach that provides more accurate health risk assessments [5][7][10]. Research Highlights - Traditional health risk assessment tools like BMI are inadequate as they do not differentiate between fat and muscle or show fat distribution, leading to varying health risks among individuals with the same BMI [7]. - A study utilizing AI technology analyzed over 33,000 MRI scans from the UK Biobank, revealing that visceral fat around organs and intramuscular fat are closely linked to higher risks of diabetes and cardiovascular diseases, even when considering BMI and waist circumference [7][10]. - The study found that low muscle mass in men increases the risk of related health issues, a trend not observed in women, indicating potential gender differences in the impact of fat distribution and muscle quality on health risks [9]. Significance of the Research - The research demonstrates that AI can enhance the precision of health risk assessments by analyzing body scan images, identifying that fat around organs and within muscles poses greater health threats compared to fat in other areas [10]. - The findings suggest that widespread adoption of this AI technology could enable earlier identification of high-risk individuals and facilitate personalized prevention and management strategies for chronic diseases like diabetes and heart disease [10]. - This advancement not only offers a new tool for the medical community but also holds promise for improving public health management through more accurate risk assessments [10].