Readability and quality assessment of AI-powered chatbot responses to overactive bladder patient questions: a comparative study

Abstract

INTRODUCTION: Overactive bladder (OAB) is a common urological condition affecting millions of people worldwide, significantly reducing their quality of life. Patient compliance and active participation in disease management are critical to achieving successful outcomes. This study aims to understand the potential role of AI-assisted chatbots in educating patients with OAB and their impact on health literacy. METHODS: We compared responses from four AI chatbots (ChatGPT, DeepSeek, Claude, and Gemini) to 16 standardized questions from the AUA Overactive Bladder Patient Guide. Two board-certified urologists independently evaluated responses using Ensuring Quality Information for Patients (EQIP) tool and Google E-E-A-T principles. RESULTS: Inter-rater reliability was excellent (ICC = 0.97 for EQIP, κ = 0.89 for E-E-A-T). A significant difference was found between chatbots in terms of readability scores (Gunning Fox Index p = 0.008, Flesch-Kincaid Grade Level p < 0.001), with all responses requiring education levels above the recommended 6th-8th grade. However, significant differences emerged in information quality (EQIP, p < 0.001; E-E-A-T, p < 0.001). Gemini demonstrated superior performance in both EQIP (60.2 ± 6.92) and E-E-A-T scores (13.5) compared to all other chatbots. CONCLUSION: AI chatbots show potential for patient education but produce content with readability levels too complex for general audiences. Significant quality variations exist between models. These findings emphasize the need for collaboration between healthcare professionals and AI developers to create more accessible, reliable health information systems.

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