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Large Language Models for Multidisciplinary Tumor Board Decision-Making in Primary Liver Tumors: A Retrospective

Julian Palzer^1,2, Alexander Genchev^1,2, Esref Belger^1,2

¹Department of General, Visceral, Pediatric and Transplant Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany.

|July 15, 2026

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Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.

Cancer research·2026

Large language models (LLMs) show potential in supporting multidisciplinary tumor boards (MTBs) for liver cancer. ChatGPT demonstrated substantial agreement with MTB decisions, while Claude showed limited concordance, suggesting LLMs can aid clinical reasoning.

Area of Science:

Oncology
Artificial Intelligence
Medical Informatics

Background:

Multidisciplinary tumor boards (MTBs) are essential for cancer care but resource-intensive.
Large language models (LLMs) offer a potential solution for streamlining MTB processes.
Evaluating LLM performance against established MTB decisions is crucial for assessing their utility.

Purpose of the Study:

To compare the concordance of LLM-generated recommendations with actual MTB decisions for cholangiocellular adenocarcinoma (CCA) and hepatocellular carcinoma (HCC).
To assess the performance of two distinct LLMs, ChatGPT and Claude, in replicating MTB recommendations.

Main Methods:

Retrospective analysis of 50 CCA and 50 HCC cases from 2022-2023 MTB protocols.
Comparison of institutional MTB recommendations with ChatGPT and Claude outputs using identical clinical summaries.

Keywords:

ChatGPT Claude LLM artificial intelligence cancer cholangiocellular carcinoma hepatocellular carcinoma large language models multidisciplinary tumor board primary liver malignancies

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Statistical analysis using Cohen's kappa for agreement and Spearman's rank for correlation.

Main Results:

ChatGPT showed 80% concordance for CCA (k=0.688) and 66% for HCC (k=0.604) with MTBs.
Claude demonstrated lower concordance: 56% for CCA and 38% for HCC.
LLM outputs were generally more guideline-oriented, while MTBs offered more individualized recommendations.

Conclusions:

ChatGPT exhibits substantial concordance with MTB decisions, indicating its potential as a supportive tool.
Claude's performance was significantly lower, highlighting model variability in clinical decision support.
LLMs show promise for enhancing structured clinical reasoning and guideline adherence in oncology, warranting further investigation in real-world settings.