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Assessing the quality and guideline-concordance of AI-powered chatbots in nutritional management of diabetes
Maryame Ben Lafqih1, Faiçal Ait Lahbib2, Kaltoum Boutahar3
1Faculty of Medical Sciences, UM6P Hospitals, University Mohammed VI Polytechnic, Benguerir, 43150, Morocco.
Aims:
This study assessed the performance of ChatGPT and Gemini in delivering evidence-based, personalized dietary recommendations for diabetes management.
Methods:
Fifty-two simulated patient cases covering nine diabetes-related categories were analyzed. We evaluated nutritional recommendations from ChatGPT and Gemini using the Analytic Hierarchy Process (AHP), focusing on clinical relevance, alignment with guidelines, personalization, and practicality. Chatbot performance was compared both overall and across diabetes categories.
Results:
Gemini (version 1.5 Flash) significantly outperformed ChatGPT (GPT-4) in overall AHP scores (Gemini: 4.34 (IQR 3.85-4.56), ChatGPT: 3.79 (IQR 3.28-4.30), p < 0.001). Gemini performed better in complex cases, particularly chronic kidney disease cases (p = 0.047) and malnutrition or post-bariatric surgery (p = 0.003). No significant differences were observed in other categories (p > 0.05).
Conclusions:
Gemini outperformed ChatGPT, showing higher guideline alignment. These findings support refining AI tools for reliable, personalized nutrition in diabetes care.
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