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Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking (2412.01605v1)

Published 2 Dec 2024 in cs.CL and cs.AI

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 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.

Citations (1)

Summary

  • The paper introduces MedChain, a novel dataset and benchmark capturing personalization, interactivity, and sequentiality in clinical cases, and MedChain-Agent, an interactive multi-agent system with feedback for improved clinical decision-making.
  • MedChain includes 12,163 cases across five clinical stages, while MedChain-Agent uses a feedback mechanism and MedCase-RAG to handle dynamic information and sequential decisions effectively.
  • Evaluations show MedChain-Agent significantly outperforms existing methods on sequential clinical tasks, demonstrating substantial gains in adaptability and accuracy for LLM agents in healthcare.

The paper presents a robust framework for enhancing the performance of LLM agents in Clinical Decision-Making (CDM) by proposing a new dataset and evaluation benchmark (MedChain) and introducing MedChain-Agent, which involves an interactive feedback-driven multi-agent system tailored for healthcare applications. The researchers identify key challenges in deploying LLM technologies for CDM, focusing on personalization, interactivity, and sequentiality, which are not adequately captured by existing medical benchmarks like licensing exams or question-answering tasks.

MedChain Dataset

MedChain is a novel dataset composed of 12,163 clinical cases that span five key stages of clinical workflow:

  1. Specialty referral
  2. History-taking
  3. Examination
  4. Diagnosis
  5. Treatment

Each case is structured to reflect real-world clinical practice, emphasizing:

  • Personalization: Detailed patient-specific information is encoded within each clinical case.
  • Interactivity: Requires dynamic information gathering through doctor-patient interaction.
  • Sequentiality: Clinical decisions in earlier stages influence subsequent ones, mirroring real clinical workflows.

MedChain-Agent Framework

MedChain-Agent is proposed to tackle real-world CDM challenges through a sophisticated multi-agent collaboration model which integrates:

  • Feedback Mechanism: Allows iterative refinement of decisions based on real-time evaluations made by a Feedback Agent, effectively mitigating error propagation.
  • MedCase-RAG Module: A Retrieval-Augmented Generation approach optimized for medical datasets that dynamically expand its database and employ structured data representations for efficient information retrieval.

Evaluation and Experimental Results

The evaluation of MedChain-Agent demonstrates significant performance improvements over existing LLM-based frameworks and benchmarks, particularly in complex, interdependent clinical scenarios outlined by MedChain. The multi-layered, iterative agent system surpasses other state-of-the-art methods in the sequential clinical tasks, as highlighted by:

  • Substantial gains in adaptability when gathering and handling information dynamically across the clinical stages.
  • Enhanced task completion rates and accuracy as compared to individual LLM deployments without feedback mechanisms.
  • A comprehensive ablation paper validating the contribution of MedCase-RAG and feedback processes in achieving the notable performance metrics.

Contributions

The contributions of this work are multi-fold:

  • It establishes a comprehensive CDM benchmark capturing the nuanced complexities of real-world medical practice beyond what current benchmarks offer.
  • Proposes a novel multi-agent collaborative approach with MedChain-Agent that leverages LLMs for integrated, feedback-driven decision making.
  • Demonstrates the effectiveness of the proposed techniques through extensive experimentation, highlighting MedChain-Agent's superiority in handling the intricacies of sequential clinical decision-making.

Overall, the framework and methodologies introduced in this paper offer a robust platform for advancing the application of LLMs in healthcare CDM, reflecting a sophisticated understanding of clinical workflows and emphasizing the importance of feedback and interactivity in accurate medical analysis and decision-making processes.