The imitation game: large language models versus multidisciplinary tumor boards: benchmarking AI against 21 sarcoma centers from the ring trial
Despite the considerable heterogeneity observed in MTB recommendations, the significant discrepancies and potentially harmful recommendations highlight current AI tools' limitations, underscoring that referral to high-volume sarcoma centers remains essential for optimal patient care. At the same time, LLMs could serve as an excellent tool to prepare for MDT discussions.
Authors
- Kamal Hummedah (ORCID: https://orcid.org/0009-0007-4390-8624)
- Franka Menge
- Christoph Reißfelder (ORCID: https://orcid.org/0000-0003-4591-1046)
- Cheng‐Peng Li (ORCID: https://orcid.org/0000-0002-6798-9541)
- Siyer Roohani (ORCID: https://orcid.org/0000-0002-8232-4994)
- Jens Jakob (ORCID: https://orcid.org/0000-0002-8499-7172)
- Markus Albertsmeier (ORCID: https://orcid.org/0000-0002-5028-5062)
- Bernd Kasper (ORCID: https://orcid.org/0000-0001-8425-8356)
- Peng Yang (ORCID: https://orcid.org/0000-0003-0144-2575)
- Aimé Terence Kalisa
Institutions
- Peking University (CN)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- LMU Klinikum (DE)
- Humboldt-Universität zu Berlin (DE)
- Peking University Cancer Hospital (CN)
- German Cancer Society (DE)
- German Centre for Cardiovascular Research (DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- University Medical Centre Mannheim (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2025-09-10
- DOI
- https://doi.org/10.1007/s00432-025-06304-9
- Citations
- 3
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56