ArticleLangenbeck's archives of surgery2026
Concordance between GPT-4 and a multidisciplinary tumor board in pancreatic cancer: A prospective pilot study.
Article in Langenbeck's archives of surgery, 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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12 authors.
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Abstract
backgroundLarge language models (LLMs) such as GPT-4 are being evaluated for their use as supportive tools in oncological treatment planning. However, in pancreatic cancer, current studies are confined to predefined question-answer formats, while studies specifically investigating real-world scenarios that benchmark LLM performance against multidisciplinary tumor board (MDT) decisions are lacking.
methodsThis prospective comparative analysis evaluated treatment and diagnostic recommendations for patients with newly diagnosed or suspected pancreatic cancer between an MDT and GPT-4. Using MDT referrals, clinical data were entered into a clinical data matrix and submitted to GPT-4 for therapeutic and diagnostic recommendations. Outputs were assessed before and after additional prompting with 41 high-ranking abstracts relevant to pancreatic cancer care. The primary endpoint was the concordance of recommendations between the MDT and GPT-4 before and after literature-based prompting.
resultsBetween September 1, 2024 and March 31, 2025, 45 patients were enrolled. The overall concordance rate between the MDT and GPT-4 was 73.3% (κ = 0.64, p < 0.0001) and did not improve following literature prompting. Discordance most often occurred in complex clinical scenarios. Concordance was highest in cases of metastatic disease (90.0%) and in neoadjuvant settings (90.0%) while it was lowest in patients requiring additional diagnostic workup (50.0%).
conclusionsGPT-4 demonstrated substantial agreement with MDT recommendations in patients with newly diagnosed or suspected pancreatic cancer. However, specific abstract prompting did not enhance the rate of concordance and GPT-4's limitations in individualized or complex contexts underscore the need for a cautious future integration into oncologic workflows.
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