aceEvaluation of large language models for clinical decision support in renal cell carcinoma: a multidisciplinary Delphi study
Aim: Large language models (LLMs) may facilitate the interpretation of clinical guideline information, but their performance in renal cell carcinoma (RCC) remains insufficiently characterized. We evaluated agreement between a multidisciplinary expert panel and the responses generated by Claude, Gemini and ChatGPT to RCC guideline-based questions. Materials & methods: A multidisciplinary tumor board developed 25 clinical questions based on the 2025 European Association of Urology RCC guidelines. Guideline figures…
When given structured guideline summaries, Gemini won expert consensus agreement on 24 of 25 kidney cancer questions (96%), while Claude and ChatGPT each won it on 23 of 25 (92%).
Panelists found occasional factual inaccuracies, omitted options and limited clinical nuance, and responses that failed consensus mostly involved clinically uncertain or individualized scenarios.
The models answered only 25 questions from structured guideline summaries, so the results do not demonstrate independent clinical reasoning or real-world decision-making performance.
Evidence
- Peer-reviewedJournal of Comparative Effectiveness Research2026-11-04
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Truvace Impact Record TRV-2026-2372, v1: “Evaluation of large language models for clinical decision support in renal cell carcinoma: a multidisciplinary Delphi study.” Truvace, 2026-11-05. /record/TRV-2026-2372 (accessed at citation time). sha256 70a0250cf2217f6e…
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