Reliable Enough? Benchmarking LLMs for Clinical Concept Extraction.

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Bibliographic Details
Title:	Reliable Enough? Benchmarking LLMs for Clinical Concept Extraction.
Authors:	Pignat J; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH.; Division of Medical Information Sciences, Hôpitaux Universitaires de Genève, CH.; Department of Radiology and Medical Informatics, Université de Genève, CH., Liakopoulos P; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH., Bonjour J; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH., Cheng P; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH., Varelogiannis S; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH., Lovis C; Division of Medical Information Sciences, Hôpitaux Universitaires de Genève, CH.; Department of Radiology and Medical Informatics, Université de Genève, CH., Bjelogrlic M; Division of Medical Information Sciences, Hôpitaux Universitaires de Genève, CH.; Department of Radiology and Medical Informatics, Université de Genève, CH., Gaudet-Blavignac C; Division of Medical Information Sciences, Hôpitaux Universitaires de Genève, CH.; Department of Radiology and Medical Informatics, Université de Genève, CH., Cuendet MA; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH., Michielin O; Division of Precision Oncology, Hôpitaux Universitaires de Genève, CH.
Source:	Studies in health technology and informatics [Stud Health Technol Inform] 2026 May 21; Vol. 336, pp. 786-787.
Publication Type:	Journal Article
Journal Info:	Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1879-8365
DOI:	10.3233/SHTI260285