Do bots provide correct and adequate guidance regarding acidity: A blinded comparison rated by patients and physicians.

Abstract

Background: Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs.

Aim: To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease).

Methods: Thirty-nine frequently asked questions were submitted to each model. Responses were independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability; and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness. Readability indices were also analyzed.

Results: Significant inter-model differences were observed across multiple physician-rated domains. Gemini-2.5 and Claude-4 achieved higher mean scores for accuracy, comprehensiveness, and actionability compared with ChatGPT-5 (P < 0.05), while Claude-4 demonstrated the highest empathy scores. Patient ratings indicated uniformly high comprehensibility across all models; however, Gemini-2.5 and Claude-4 responses were perceived as more actionable than those generated by ChatGPT-5. Readability analysis showed that ChatGPT-5 produced the most accessible responses, corresponding approximately to a high-school reading level, whereas Gemini-2.5 and Claude-4 generated more linguistically complex content.

Conclusion: These findings underscore the need for careful model selection and suggest that hybrid approaches integrating complementary model strengths may optimize safe and effective artificial intelligence -assisted patient education in gastroenterology.