Comparative Analysis of ChatGPT and Gemini in Addressing Questions from Chronic Kidney Disease Patients

Abstract

Background: Chronic kidney disease (CKD) is a major global health burden. Patient education is a crucial part of CKD management. Large language models (LLMs) such as ChatGPT and Gemini may help patients access medical information, but their reliability in CKD-related contexts is uncertain. Methods: We collected 291 questions from 100 CKD patients and selected and analyzed 123 of them across three categories: medical condition and treatment, nutrition and diet, and symptom management. Responses from ChatGPT and Gemini were assessed by two nephrology specialists using the Quality Assessment of Medical Artificial Intelligence (QAMAI) scale. Results: When all 123 questions were evaluated together, ChatGPT outperformed Gemini in terms of clarity and usefulness. However, when the questions were analyzed by category, Gemini demonstrated relatively stronger performance in the nutrition and symptom management domains. Accuracy and relevance were comparable between the two models. Neither consistently provided adequate citations. Conclusion: ChatGPT and Gemini demonstrate potential as supplementary tools for CKD patient education, with complementary strengths across different domains. Although they cannot replace clinical expertise, their supervised use could enhance information access and reduce clinician burden.

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