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Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

18 June 2024
Rui Yang
Yilin Ning
Emilia Keppo
Mingxuan Liu
Chuan Hong
Danielle S Bitterman
J. Ong
Daniel Ting
Nan Liu
    3DV
    MedIm
    RALM
ArXivPDFHTML
Abstract

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

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