Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

Rui Yang; Yilin Ning; Emilia Keppo; Mingxuan Liu; Chuan Hong; Danielle S Bitterman; Jasmine Chiat Ling Ong; Daniel Shu Wei Ting; Nan Liu

arXiv · 2406.12449

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

Rui Yang·Yilin Ning·Emilia Keppo·Mingxuan Liu·Chuan Hong·Danielle S Bitterman·Jasmine Chiat Ling Ong·Daniel Shu Wei Ting·Nan Liu

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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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Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle S Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu. 2024-06-18. Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine. https://arxiv.org/abs/2406.12449

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