Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong +4 more
6/18/2024
cs.AI

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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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Cite this paper

@article{yang2024retrievalaugmented,
  title  = {Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine},
  author = {Rui Yang and Yilin Ning and Emilia Keppo and Mingxuan Liu and Chuan Hong and Danielle S Bitterman and Jasmine Chiat Ling Ong and Daniel Shu Wei Ting and Nan Liu},
  year   = {2024},
  eprint = {2406.12449},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.12449v1}
}

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