Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866929390883962880 |
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| author | Yang, Rui Ning, Yilin Keppo, Emilia Liu, Mingxuan Hong, Chuan Bitterman, Danielle S Ong, Jasmine Chiat Ling Ting, Daniel Shu Wei Liu, Nan |
| author_facet | Yang, Rui Ning, Yilin Keppo, Emilia Liu, Mingxuan Hong, Chuan Bitterman, Danielle S Ong, Jasmine Chiat Ling Ting, Daniel Shu Wei Liu, Nan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12449 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine Yang, Rui Ning, Yilin Keppo, Emilia Liu, Mingxuan Hong, Chuan Bitterman, Danielle S Ong, Jasmine Chiat Ling Ting, Daniel Shu Wei Liu, Nan Artificial Intelligence 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. |
| title | Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.12449 |