Evidence map›Paper›PMID 42090098›Full record

ArticleInternational urology and nephrology2026

Alignment between AI clinical decision tools and multidisciplinary tumor board decisions in prostate cancer.

Akif Koc, Oguzhan Akpinar, Alper Keskin, Halil Ibrahim Tarhan, Muhammet Guzelsoy

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Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Akif KocDepartment of Urology, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa School of Medicine, University of Health Sciences, Bursa, Turkey. akifkoc@yahoo.com.ORCID http://orcid.org/0000-0002-9780-9732
Oguzhan AkpinarDepartment of Urology, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey.ORCID http://orcid.org/0000-0003-3492-8467
Alper KeskinDepartment of Urology, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey.ORCID http://orcid.org/0000-0003-1589-322X
Halil Ibrahim TarhanDepartment of Urology, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa, Turkey.ORCID http://orcid.org/0009-0003-6835-9386
Muhammet GuzelsoyDepartment of Urology, Bursa Yuksek Ihtisas Training and Research Hospital, Bursa School of Medicine, University of Health Sciences, Bursa, Turkey.ORCID http://orcid.org/0000-0002-7249-4332

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to evaluate the concordance between treatment recommendations generated by LLMs and decisions made by a multidisciplinary uro-oncology tumor board.

methodsForty-eight consecutive prostate cancer cases previously discussed at a multidisciplinary tumor board were retrospectively analyzed. For each case, treatment recommendations were generated using five LLM platforms (ChatGPT-4o, ChatGPT, Perplexity, Copilot, and DeepSeek) based on standardized clinical summaries. Four independent urology specialists evaluated the concordance between LLM recommendations and tumor board decisions using a 5-point Likert scale. Differences among models were assessed using the Friedman test followed by Bonferroni-corrected Wilcoxon signed-rank tests. Inter-rater agreement was calculated using the intraclass correlation coefficient.

resultsSignificant differences in concordance were observed among the evaluated AI platforms (χ

conclusionLarge language models can demonstrate substantial concordance with multidisciplinary tumor board decisions in prostate cancer management. However, variability among models and the risk of hallucinated information indicate that LLMs should function as clinical decision-support tools under expert supervision rather than as autonomous decision-makers.

Indexed as

Artificial IntelligenceClinical Decision-MakingDecision Support Systems, ClinicalProstatic NeoplasmsHumansLarge Language ModelsMaleRetrospective StudiesArtificial intelligenceClinical decision supportLarge language modelsMultidisciplinary tumor boardProstate cancer

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.