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#large language models Open access Sep 2026

Large Language Models Versus Multidisciplinary Tumor Board Decisions in Thyroid Cancer

ABSTRACT Objectives Large language models (LLMs) are increasingly proposed as clinical decision‐support tools; however, their agreement with real‐world multidisciplinary tumor board (MDT) decisions remains insufficiently investigated in thyroid oncology. To evaluate the concordance between treatment recommendations gen...

B. B. Büyük, Arzu Or Koca, F. Toprak et al. · 0 citations
Open access Sep 2026

Concordance between GPT-4 and a multidisciplinary tumor board in pancreatic cancer: A prospective pilot study

Large 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 performa...

F. Gehrisch, K. Kirkgöz, Antonie Willner et al. · 0 citations
Review Open access Aug 2026

Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards

Both models demonstrated high agreement with expert GIST MTB recommendations, with no significant performance difference between them, and support a potential assistive role for LLMs in GIST MTB workflows, while underscores the continued necessity of expert oversight.

Reza Dehdab, Judith Herrmann, Fiona Mankertz et al. · 0 citations
Open access Aug 2026

Impact of Molecular Classification on Multidisciplinary Treatment Decision Making in Early-Stage Endometrial Cancer: A Prospective Real-World Study From India.

PURPOSE Molecular classification has refined risk stratification in endometrial cancer and is now incorporated into the 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system. However, prospective real-world data on its impact on multidisciplinary tumor board (MDT) decision making remain limit...

R. Pinninti, H. Abbaraju, Krishna Mohan Mallavarapu et al. · 1 citation
Jul 2026

Accuracy and Safety of Large Language Models in Endometrial Cancer Decision Making: A Case-Based In Silico Benchmarking Study.

PURPOSE To compare the concordance of ChatGPT, Gemini, and Claude with a prespecified expert guideline-based reference standard in fabricated endometrial cancer clinical vignettes under standardized prompting. METHODS We conducted a case-based in silico benchmarking study using 35 fabricated postoperative endometrial...

E. Perrone, Giuseppe Parisi, M. Giuliano et al. · 0 citations

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