Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking

• URL: http://arxiv.org/abs/2412.01605v1
• Date: Mon, 02 Dec 2024 15:25:02 GMT
• Title: Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking
• Authors: Jie Liu, Wenxuan Wang, Zizhan Ma, Guolin Huang, Yihang SU, Kao-Jung Chang, Wenting Chen, Haoliang Li, Linlin Shen, Michael Lyu,
• Abstract summary: We present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow. 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.
• Score: 58.25862290294702
• License:
• Abstract: 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. The relevant dataset and code will be released upon acceptance of this paper.

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