ArticleInvestigative and clinical urology2025
Assessment of artificial intelligence performance in answering questions on onabotulinum toxin and sacral neuromodulation.
Article in Investigative and clinical urology, 2025. 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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Who cites it
1 citing paper in PubMed.
- Estimation of IPSS and OABSS scores using ChatGPT-4o: a comparative validation study in Korea.BMC urology · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to evaluate the performance of three artificial intelligence (AI) models-ChatGPT, Gemini, and Copilot-in addressing clinically relevant questions about onabotulinum toxin and sacral neuromodulation (SNM) for the management of overactive bladder (OAB). MATERIALS AND
methodsA set of 30 questions covering mechanisms of action, indications, contraindications, procedural details, efficacy, and safety profiles was posed to each AI model. Responses were assessed by a panel of four urology specialists using predefined criteria: accuracy, completeness, clarity, and consistency. A multi-dimensional scoring framework evaluated the performance across five dimensions: factual accuracy, relevance, clarity/coherence, structure, and utility. Responses were scored on a 4-point Likert scale, and statistical analyses were conducted using one-way ANOVA to compare model performance.
resultsChatGPT achieved the highest mean score (3.98/4) across all dimensions, with statistically significant differences compared to Gemini (3.20/4) and Copilot (2.60/4) (p=0.001 for all dimensions). ChatGPT excelled particularly in clinical application, procedure, and safety categories, consistently delivering accurate and comprehensive answers. No statistically significant differences were found between Gemini and Copilot in most categories.
conclusionsChatGPT demonstrated superior performance in generating accurate, complete, and clinically relevant responses for OAB management, highlighting its potential as a reliable tool for both healthcare professionals and patients. However, the variability observed in Gemini and Copilot underscores the need for further refinement of these models. Future studies should explore real-world integration of AI models into clinical workflows to enhance patient care and decision-making.
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Registered trials
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