@inproceedings{lu-etal-2024-clinicalrag,
title = "{C}linical{RAG}: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval",
author = "Lu, Yuxing and
Zhao, Xukai and
Wang, Jinzhuo",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.6",
pages = "64--68",
abstract = "Large Language Models (LLMs) have revolutionized text generation across diverse domains, showcasing an ability to mimic human-like text with remarkable accuracy. Yet, these models frequently encounter a significant hurdle: producing hallucinations, a flaw particularly detrimental in the healthcare domain where precision is crucial. In this paper, we introduce ClinicalRAG, a novel multi-agent pipeline to rectify this issue by incorporating heterogeneous medical knowledge{---}both structured and unstructured{---}into LLMs to bolster diagnosis accuracy. ClinicalRAG can extract related medical entities from user inputs and dynamically integrate relevant medical knowledge during the text generation process. Comparative analyses reveal that ClinicalRAG significantly outperforms knowledge-deficient methods, offering enhanced reliability in clinical decision support. This advancement marks a pivotal proof-of-concept step towards mitigating misinformation risks in healthcare applications of LLMs.",
}
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%0 Conference Proceedings
%T ClinicalRAG: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval
%A Lu, Yuxing
%A Zhao, Xukai
%A Wang, Jinzhuo
%Y Li, Sha
%Y Li, Manling
%Y Zhang, Michael JQ
%Y Choi, Eunsol
%Y Geva, Mor
%Y Hase, Peter
%Y Ji, Heng
%S Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F lu-etal-2024-clinicalrag
%X Large Language Models (LLMs) have revolutionized text generation across diverse domains, showcasing an ability to mimic human-like text with remarkable accuracy. Yet, these models frequently encounter a significant hurdle: producing hallucinations, a flaw particularly detrimental in the healthcare domain where precision is crucial. In this paper, we introduce ClinicalRAG, a novel multi-agent pipeline to rectify this issue by incorporating heterogeneous medical knowledge—both structured and unstructured—into LLMs to bolster diagnosis accuracy. ClinicalRAG can extract related medical entities from user inputs and dynamically integrate relevant medical knowledge during the text generation process. Comparative analyses reveal that ClinicalRAG significantly outperforms knowledge-deficient methods, offering enhanced reliability in clinical decision support. This advancement marks a pivotal proof-of-concept step towards mitigating misinformation risks in healthcare applications of LLMs.
%U https://aclanthology.org/2024.knowllm-1.6
%P 64-68
Markdown (Informal)
[ClinicalRAG: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval](https://aclanthology.org/2024.knowllm-1.6) (Lu et al., KnowLLM-WS 2024)
ACL