Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease

<b>Background</b>: Chronic kidney disease (CKD) requires strict dietary management tailored to disease stage and individual needs. Recent advances in artificial intelligence (AI) have introduced chatbot-based tools capable of generating dietary recommendations. However, their accuracy, personalization, and practical applicability in clinical nutrition remain largely unvalidated, particularly in non-Western settings. <b>Methods</b>: Simulated patient profiles representing each CKD stage were developed and used to prompt GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft) with the same request for meal planning. AI-generated diets were evaluated by three physicians using a 5-point Likert scale across three criteria: personalization, consistency with guidelines, practicality, and availability. Descriptive statistics, Kruskal-Wallis tests, and Dunn's post hoc tests were performed to compare model performance. Nutritional analysis of four meal plans (Initial, GPT-4, Gemini, and Copilot) was conducted using both GPT-4 estimates and manual calculations validated against clinical dietary sources. <b>Results</b>: Scores for personalization and consistency were significantly higher for Gemini and GPT-4 compared with Copilot, with no significant differences between Gemini and GPT-4 (<i>p</i> = 0.0001 and <i>p</i> = 0.0002, respectively). Practicality showed marginal significance, with GPT-4 slightly outperforming Gemini (<i>p</i> = 0.0476). Nutritional component analysis revealed discrepancies between GPT-4's internal estimations and manual values, with occasional deviations from clinical guidelines, most notably for sodium and potassium, and moderate overestimation for phosphorus. <b>Conclusions</b>: While AI chatbots show promise in delivering dietary guidance for CKD patients, with Gemini demonstrating the strongest performance, further development, clinical validation, and testing with real patient data are needed before AI-driven tools can be fully integrated into patient-centered CKD nutritional care.