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TRUVACE RECORD VERSION
record: TRV-2026-2372
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-05T00:01:56.359972Z
status: published
lens: trace
sector: health
headline: Evaluation of large language models for clinical decision support in renal cell carcinoma: a multidisciplinary Delphi study
dek: 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…
gain_title: 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%).
problem_title: Panelists found occasional factual inaccuracies, omitted options and limited clinical nuance, and responses that failed consensus mostly involved clinically uncertain or individualized scenarios.
trace_subject: AI chatbot answers to renal cell carcinoma guideline questions
gain_reading: 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%).
gain_evidence: Consensus in agreement was reached for 24/25 Gemini responses (96%) and 23/25 responses (92%) for both Claude and ChatGPT.
problem_reading: Panelists found occasional factual inaccuracies, omitted options and limited clinical nuance, and responses that failed consensus mostly involved clinically uncertain or individualized scenarios.
problem_evidence: Panelists identified occasional factual inaccuracies, omitted options and limited clinical nuance. | Responses requiring further discussion or failing to reach consensus mainly involved clinically uncertain or individualized scenarios.
quick_read: An 11-member multidisciplinary panel rated Claude, Gemini and ChatGPT answers to 25 questions drawn from the 2025 European Association of Urology renal cell carcinoma guidelines. Gemini reached consensus agreement on 24 responses (96%); Claude and ChatGPT each reached it on 23 (92%). No response reached consensus in disagreement.

Using a median score of at least 7 on a 9-point scale, Gemini was rated acceptable on all 25 answers, Claude on 24 (96%) and ChatGPT on 23 (92%). The differences among the models were not statistically significant overall or in pairwise comparisons.

The weaker answers clustered in clinically uncertain or individualized scenarios, and panelists noted occasional factual errors, omitted options and limited nuance. The authors say the results show potential for guideline interpretation but not independent clinical reasoning or real-world decision-making, so expert oversight remains essential.
limitation: 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.
tag: Dual reading
key_points: A multidisciplinary tumor board wrote 25 clinical questions based on the 2025 European Association of Urology renal cell carcinoma guidelines. | Guideline figures and tables were converted into structured, plain-language text summaries before being given to the models. | Each model produced one response per question using direct prompting. | An 11-member expert panel scored responses in a two-round modified Delphi process on a 9-point scale with predefined consensus criteria. | Consensus in agreement: Gemini 24/25 (96%), Claude 23/25 (92%), ChatGPT 23/25 (92%); none reached consensus in disagreement. | No statistically significant differences were found between the models. | Questions that needed more discussion or failed consensus mainly involved clinically uncertain or individualized scenarios.
rundown: The study tested whether large language models can interpret guideline content for kidney cancer. A multidisciplinary tumor board built 25 questions from the 2025 European Association of Urology guidelines, and the guideline figures and tables were turned into structured plain-language summaries so the models could use them.

Claude, Gemini and ChatGPT each answered every question once with direct prompting. Eleven experts rated the answers in two modified Delphi rounds on a 9-point scale using predefined consensus criteria.

Gemini led numerically, with consensus agreement on 96% of answers and a median score of at least 7 on all 25, against 92% consensus for both Claude and ChatGPT. Medians of at least 7 were reached for 24 Claude and 23 ChatGPT answers, and the gaps were not statistically significant.

Panelists flagged occasional factual inaccuracies, omitted options and limited clinical nuance, with trouble concentrated in uncertain or individualized cases. The authors conclude that the models align frequently with expert consensus when given structured material, but this does not show independent reasoning or real-world performance.
sources:
- peer_reviewed | Journal of Comparative Effectiveness Research | https://doi.org/10.57264/cer-2026-0082 | 2026-11-04
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