Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
<|reference_start|>Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine: 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.<|reference_end|>
@article{yang2024retrieval-augmented,
    title={Retrieval-Augmented Generation for Generative Artificial Intelligence in
  Medicine},
    author={Rui Yang, Yilin Ning, Emilia Keppo, Mingxuan Liu, Chuan Hong, Danielle
  S Bitterman, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting, Nan Liu},
    journal={arXiv preprint arXiv:2406.12449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12449},
    primaryClass={cs.AI}
}