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.- Anthology ID:
- 2024.knowllm-1.6
- Volume:
- Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Sha Li, Manling Li, Michael JQ Zhang, Eunsol Choi, Mor Geva, Peter Hase, Heng Ji
- Venues:
- KnowLLM | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 64–68
- Language:
- URL:
- https://aclanthology.org/2024.knowllm-1.6
- DOI:
- Cite (ACL):
- Yuxing Lu, Xukai Zhao, and Jinzhuo Wang. 2024. ClinicalRAG: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval. In Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024), pages 64–68, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- ClinicalRAG: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval (Lu et al., KnowLLM-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.knowllm-1.6.pdf