This publisher's note contains a correction to Opt. Lett.50, 241 (2025)10.1364/OL.536982.
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Enhancing Large Language Models with Retrieval-augmented Generation: A Radiology-specific Approach.
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Department of Radiology & Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, Calif.
Retrieval-augmented generation (RAG) is a strategy to improve performance of large language models (LLMs) by providing the LLM with an updated corpus of knowledge that can be used for answer generation in real-time. RAG may improve LLM performance and clinical applicability in radiology by providing citable, up-to-date information without requiring model fine-tuning. In this retrospective study, a radiology-specific RAG was developed using a vector database of 3,689 articles published from January 1999 to December 2023.
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Department of Radiology, Duke University Hospital, 2301 Erwin Rd, Durham, NC 27710.
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