Medical Entity Linking in Low-Resource Settings with Fine-Tuning-Free LLMs.

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Bibliographic Details
Title: Medical Entity Linking in Low-Resource Settings with Fine-Tuning-Free LLMs.
Authors: Seeha S; Chair of Medical Informatics, Institute of AI and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany., Boeker M; Chair of Medical Informatics, Institute of AI and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany., Modersohn L; Chair of Medical Informatics, Institute of AI and Informatics in Medicine (AIIM), TUM University Hospital, Technical University of Munich, Munich, Germany.
Source: Studies in health technology and informatics [Stud Health Technol Inform] 2025 Sep 03; Vol. 331, pp. 245-254.
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/SHTI251402