Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.

Journal: Journal of the American Medical Informatics Association : JAMIA

PMID: 39812777

Abstract

OBJECTIVE: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.

Authors

Siru Liu

School of Medicine, University of Utah, Salt Lake City, Utah, US.
Allison B McCoy

Vanderbilt University Medical Center, Nashville, TN.
Adam Wright

Harvard Medical School, Boston, MA; Brigham and Women's Hospital, Boston, MA.

Keywords

Humans Information Storage and Retrieval Large Language Models Natural Language Processing Programming Languages

External Resources

View on PubMed Access via DOI PubMed (39812777)

Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.

Abstract

Authors

Keywords

External Resources

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