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Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

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arxiv 2406.12449 v1 pith:S6I4Q4VS submitted 2024-06-18 cs.AI

Rui Yang , Yilin Ning , Emilia Keppo , Mingxuan Liu , Chuan Hong , Danielle S Bitterman , Jasmine Chiat Ling Ong , Daniel Shu Wei Ting
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Nan Liu
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classification cs.AI
keywords generativeartificialgenerationinnovationsintelligencemedicineretrieval-augmentedaccurate
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology

    cs.CL 2025-07 reject novelty 4.0 of 10

    In a 10-case rheumatology benchmark, a 46-billion-parameter model with retrieval-augmented generation scored highest on diagnosis and treatment, but the result lacks error bars and a released dataset.

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