Generative AI responses to exercise-related questions asked by patients with multiple sclerosis: An evaluation of quality, accuracy, and readability

Objective This study aims to compare the quality, accuracy, reliability, and readability of responses produced by ChatGPT-5.4 Thinking and Google Gemini 3 Flash regarding frequently asked questions by multiple sclerosis (MS) patients about exercise. Method A total of 75 questions were evaluated. Expert physiotherapists analysed the responses utilizing the Global Quality Score (GQS), Modified DISCERN (mDISCERN), Likert Accuracy Scale, and the Flesch Reading Ease (FRE). Results While 61.3% of ChatGPT responses were classified as high quality, this rate was 24% for Gemini (p<0.001). ChatGPT-generated responses demonstrated significantly higher overall accuracy and mDISCERN scores than those generated by Gemini (p<0.001). Categorical analyses revealed that the responses generated by ChatGPT received higher accuracy in the domains of exercise planning, safety and risk management, as well as participation and self-management. Additionally, ChatGPT-generated responses received significantly higher mDISCERN scores in the exercise planning and safety categories. No significant difference was observed between the two models regarding overall FRE scores (p>0.05). The mean FRE scores for ChatGPT and Gemini were 41.70 and 38.21, respectively, with responses from both models classified at a "difficult" readability level. Conclusion Within the scope of this study, ChatGPT-generated responses demonstrated higher quality, accuracy, and reliability than those generated by Gemini. Nonetheless, patient accessibility could be limited by the poor readability metrics observed in both tools. While AI-based chatbots may show promise in reinforcing patient education for MS populations, they must not substitute specialized medical professionals during clinical decision-making.