Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study

Abstract BackgroundEmbedding models are critical components of Retrieval Augmented Generation (RAG) systems for retrieving and searching unstructured medical data. However, existing models are predominantly trained on publicly available English datasets, limiting their effecti...

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Autori principali:	Kamyar Arzideh, Henning Schäfer, Ahmad Idrissi-Yaghir, Cynthia Sabrina Schmidt, Bahadir Eryilmaz, Mikel Bahn, Amin T Turki, Olivia Barbara Pollok, Eva Maria Hartmann, Philipp Winnekens, Katarzyna Borys, Johannes Haubold, Felix Nensa, René Hosch
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	JMIR Publications 2026-03-01
Serie:	Journal of Medical Internet Research
Accesso online:	https://www.jmir.org/2026/1/e82997
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