AI and Cancer Research ── Bladder ── 2026-11-07
Bladder cancer: treatment, trials and AI
- Abstract
Aim: To assess expert panel agreement with responses from three large language models, Claude, Gemini and ChatGPT, to questions based on guidelines for non-muscle-invasive bladder cancer (NMIBC), muscle-invasive bladder cancer (MIBC) and upper tract urothelial carcinoma (UTUC). Materials & methods: The 2025 European Association of Urology (EAU) guidelines were provided to Claude 3.5 Haiku, Gemini 2.5 Flash and GPT-4.1-mini. Figures and tables were converted into plain language text. Each model answered 25 questions per entity. Panels of 7-10 experts rated responses on a 9-point scale using a modified Delphi process with two rounds. Outcomes were consensus in agreement and median expert panel score ≥7.
Results: For Claude, Gemini and ChatGPT, respectively, consensus in agreement was achieved for 88%, 100% and 96% of NMIBC responses (p = 0.097); 92%, 100% and 96% of MIBC responses (p = 0.223); and 56%, 100% and 96% of UTUC responses (p Conclusion: Under the study conditions, large language model responses frequently achieved expert panel agreement, particularly for Gemini and ChatGPT, supporting the potential of these models for guideline interpretation. Further validation is required to establish clinical utility and safety. What is this article about? Clinical guidelines help healthcare professionals make decisions about bladder and upper urinary tract cancers, but they can be long and complex. Artificial intelligence tools such as Claude, Gemini and ChatGPT may help interpret guideline information. However, their answers need expert review before clinical use. What methodology was used in this study? Specialists created 25 questions for three clinical areas: non-muscle-invasive bladder cancer, muscle-invasive bladder cancer and upper tract urothelial carcinoma. The 2025 European Association of Urology guidelines were provided to the three tools, with figures and tables converted into clear text. Each tool answered 75 questions. Panels of 7–10 specialists reviewed the responses in two rounds. What were the results? Across all 75 questions, experts agreed with 59 Claude responses, 75 Gemini responses and 72 ChatGPT responses. The largest differences occurred in upper tract urothelial carcinoma, where experts agreed with 14 of 25 Claude responses, 25 of 25 Gemini responses and 24 of 25 ChatGPT responses. Expert comments highlighted limitations in the completeness and focus of some Claude responses. Why are these results important? These tools can often interpret supplied guideline information in a way that agrees with specialist opinion. However, the study did not assess their safety or usefulness in real clinical practice. Their responses should therefore be checked by healthcare professionals, particularly in complex situations.
Journal IF-equivalent: 2.7 (OpenAlex 2-year mean citedness, value as of 2026-11-05, retrieved 2026-11-06; not the official Clarivate IF)Reference: Guerrero-Ramos F, Fernández Gómez JM, Aparicio TF, López-Beltrán A, Rueda ÓB, González LL, et al. Evaluation of large language models for guideline-based clinical questions in muscle-invasive and non-muscle-invasive bladder cancer and upper tract urothelial carcinoma: a multidisciplinary Delphi study. J Comp Eff Res. 2026 Nov 4 [Epub ahead of print]. doi:10.57264/cer-2026-0079.Checked: Abstract only