%0 Journal Article
%A Li, Cheng-Peng
%A Kalisa, Aimé Terence
%A Roohani, Siyer
%A Hummedah, Kamal
%A Menge, Franka
%A Reißfelder, Christoph
%A Albertsmeier, Markus
%A Kasper, Bernd
%A Jakob, Jens
%A Yang, Cui
%T The imitation game: large language models versus multidisciplinary tumor boards: benchmarking AI against 21 sarcoma centers from the ring trial.
%J Journal of cancer research and clinical oncology
%V 151
%N 9
%@ 0301-1585
%C Heidelberg
%I Springer
%M DKFZ-2025-01872
%P 248
%D 2025
%X The study aims to compare the treatment recommendations generated by four leading large language models (LLMs) with those from 21 sarcoma centers' multidisciplinary tumor boards (MTBs) of the sarcoma ring trial in managing complex soft tissue sarcoma (STS) cases.We simulated STS-MTBs using four LLMs-Llama 3.2-vison: 90b, Claude 3.5 Sonnet, DeepSeek-R1, and OpenAI-o1 across five anonymized STS cases from the sarcoma ring trial. Each model was queried 21 times per case using a standardized prompt, and the responses were compared with human MTBs in terms of intra-model consistency, treatment recommendation alignment, alternative recommendations, and source citation.LLMs demonstrated high inter-model and intra-model consistency in only 20
%K Humans
%K Sarcoma: therapy
%K Sarcoma: pathology
%K Benchmarking: methods
%K Cancer Care Facilities
%K Language
%K Large Language Models
%K Artificial intelligence (Other)
%K Clinical decision (Other)
%K Large language model (Other)
%K Multidisciplinary tumor board (Other)
%K Soft tissue sarcoma (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40926110
%R 10.1007/s00432-025-06304-9
%U https://inrepo02.dkfz.de/record/304480