Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917000261926912 |
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| author | Liu, Jie Wang, Wenxuan Ma, Zizhan Huang, Guolin SU, Yihang Chang, Kao-Jung Chen, Wenting Li, Haoliang Shen, Linlin Lyu, Michael |
| author_facet | Liu, Jie Wang, Wenxuan Ma, Zizhan Huang, Guolin SU, Yihang Chang, Kao-Jung Chen, Wenting Li, Haoliang Shen, Linlin Lyu, Michael |
| contents | Clinical decision making (CDM) is a complex, dynamic process crucial to healthcare delivery, yet it remains a significant challenge for artificial intelligence systems. While Large Language Model (LLM)-based agents have been tested on general medical knowledge using licensing exams and knowledge question-answering tasks, their performance in the CDM in real-world scenarios is limited due to the lack of comprehensive testing datasets that mirror actual medical practice. To address this gap, we present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow. MedChain distinguishes itself from existing benchmarks with three key features of real-world clinical practice: personalization, interactivity, and sequentiality. Further, to tackle real-world CDM challenges, we also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses. MedChain-Agent demonstrates remarkable adaptability in gathering information dynamically and handling sequential clinical tasks, significantly outperforming existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence Liu, Jie Wang, Wenxuan Ma, Zizhan Huang, Guolin SU, Yihang Chang, Kao-Jung Chen, Wenting Li, Haoliang Shen, Linlin Lyu, Michael Computation and Language Artificial Intelligence Clinical decision making (CDM) is a complex, dynamic process crucial to healthcare delivery, yet it remains a significant challenge for artificial intelligence systems. While Large Language Model (LLM)-based agents have been tested on general medical knowledge using licensing exams and knowledge question-answering tasks, their performance in the CDM in real-world scenarios is limited due to the lack of comprehensive testing datasets that mirror actual medical practice. To address this gap, we present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow. MedChain distinguishes itself from existing benchmarks with three key features of real-world clinical practice: personalization, interactivity, and sequentiality. Further, to tackle real-world CDM challenges, we also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses. MedChain-Agent demonstrates remarkable adaptability in gathering information dynamically and handling sequential clinical tasks, significantly outperforming existing approaches. |
| title | Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.01605 |