Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence
This addresses the gap in AI systems for clinical decision making in healthcare, though it is incremental as it builds on existing LLM agent frameworks.
The authors tackled the problem of limited performance of LLM-based agents in clinical decision making by introducing MedChain, a dataset of 12,163 clinical cases that mirrors real-world practice, and MedChain-Agent, an AI system that significantly outperforms existing approaches.
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.