ArticleBMC urology2026
Readability and quality assessment of AI-powered chatbot responses to overactive bladder patient questions: a comparative study.
Article in BMC urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOveractive 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.
methodsWe 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.
resultsInter-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.
conclusionAI 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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