ArticleJournal of medical Internet research2026
Large Language Models in Multidisciplinary Decision-Making for Hepatopancreatobiliary Oncology: Retrospective Comparative Feasibility Study.
Article in Journal of medical Internet research, 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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17 authors.
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Abstract
Background: Hepatopancreatobiliary (HPB) malignancies require complex treatment planning that often relies on multidisciplinary team (MDT) discussions. Large language models (LLMs) have recently been explored for clinical decision support, but their performance within real-world multidisciplinary decision environments remains unclear. In particular, the stability of LLM-generated recommendations-that is, whether a model produces the same answer when given the same clinical input-has rarely been examined. Objective: This study aimed to evaluate the stability of treatment recommendations generated by contemporary LLMs when identical HPB cases are queried repeatedly, and their concordance with the treatment decisions reached at an institutional MDT conference. Methods: This retrospective study included consecutive cases discussed at a single-center HPB MDT conference between September 1, 2024, and August 31, 2025. Standardized clinical case summaries derived from preconference documentation were provided to 4 LLMs (GPT-4o, GPT-5.2, Gemini 3 Pro, and Claude Sonnet 4.5) through their consumer web interfaces. Each model recommended a treatment among predefined MDT treatment options, and identical queries were repeated 4 times in separate sessions. Stability was quantified as the discordance rate relative to the initial response and, without privileging any single query, as the mean pairwise agreement and Fleiss κ across the 4 iterations. Concordance with MDT decisions was assessed using both the initial and modal responses, together with Cohen κ and class-wise Results: A total of 107 MDT cases were analyzed. Stability differed significantly across models ( Conclusions: LLM-generated treatment recommendations demonstrated moderate alignment with MDT decisions in HPB oncology. Importantly, response stability varied substantially across models, indicating that concordance alone is insufficient for evaluating LLMs as clinical decision support tools. These findings suggest that LLMs may serve as a reasoning-support layer in MDT-like decision environments, but their response stability must be systematically characterized before clinical integration.
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