This study evaluates the quality and clinical applicability of artificial intelligence (AI)-generated weight management diet plans. The researchers used ChatGPT (version 4.0) to create diet plans and compared them with two control diet plans from tertiary medical centers. Experts in obesity medicine and clinical nutrition were asked to assess the AI-generated plans based on effectiveness, balancedness, comprehensiveness, flexibility, and applicability. Additionally, personalized diet plans for hypothetical patients with specific health conditions were evaluated. The study found no significant differences between the three diet plans in any evaluation category. Among the 14 experts who believed they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized diet plan was assessed, with scores above neutral for all evaluation variables. However, several limitations of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Despite these limitations, the study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care. The study also discusses the challenges of distinguishing AI-generated outputs from human writing, the limitations of AI in navigating complex dietary considerations, and the need for expert review of AI-generated diet plans before public release. The study concludes that AI-generated diet plans show promise for real-world clinical applications, but further research is needed to address the challenges and limitations identified.This study evaluates the quality and clinical applicability of artificial intelligence (AI)-generated weight management diet plans. The researchers used ChatGPT (version 4.0) to create diet plans and compared them with two control diet plans from tertiary medical centers. Experts in obesity medicine and clinical nutrition were asked to assess the AI-generated plans based on effectiveness, balancedness, comprehensiveness, flexibility, and applicability. Additionally, personalized diet plans for hypothetical patients with specific health conditions were evaluated. The study found no significant differences between the three diet plans in any evaluation category. Among the 14 experts who believed they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized diet plan was assessed, with scores above neutral for all evaluation variables. However, several limitations of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Despite these limitations, the study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care. The study also discusses the challenges of distinguishing AI-generated outputs from human writing, the limitations of AI in navigating complex dietary considerations, and the need for expert review of AI-generated diet plans before public release. The study concludes that AI-generated diet plans show promise for real-world clinical applications, but further research is needed to address the challenges and limitations identified.