Evaluating reasoning models for therapy recommendations in gastrointestinal stromal tumors: expert and LLM-based evaluations of OpenAI o1 and DeepSeek-R1

This study aims to evaluate two advanced reasoning LLMs in generating treatment recommendations for real-world gastrointestinal stromal tumor (GIST) cases and assess their concordance with multidisciplinary team (MDT) decisions at a certified tertiary sarcoma center.

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Dettagli Bibliografici
Autori principali: Harbrücker, Melissa (Autore) , Menge, Franka (Autore) , Taebi, Arshia (Autore) , Nörenberg, Dominik (Autore) , Speer, Tobias (Autore) , Reißfelder, Christoph (Autore) , Hohenberger, Peter (Autore) , Jakob, Jens (Autore) , Li, Chengpeng (Autore) , Yang, Cui (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 18 May 2026
In: Journal of cancer research and clinical oncology
Year: 2026, Volume: 152, Fascicolo: 7, Pages: 1-10
ISSN:1432-1335
DOI:10.1007/s00432-026-06489-7
Accesso online:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00432-026-06489-7
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00432-026-06489-7
Testo
Note sull'autore:Melissa Harbrücker, Franka Menge, Arshia Taebi, Dominik Nörenberg, Tobias Speer, Christoph Reißfelder, Peter Hohenberger, Jens Jakob, Chengpeng Li, Cui Yang
Descrizione
Riassunto:This study aims to evaluate two advanced reasoning LLMs in generating treatment recommendations for real-world gastrointestinal stromal tumor (GIST) cases and assess their concordance with multidisciplinary team (MDT) decisions at a certified tertiary sarcoma center.
Descrizione del documento:Online veröffentlicht: 18. Mai 2026, Artikelversion: 13. Juli 2026
Gesehen am 31.07.2026
Descrizione fisica:Online Resource
ISSN:1432-1335
DOI:10.1007/s00432-026-06489-7