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LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

Hang Yang, Hao Chen, Hui Guo, Yineng Chen, Ching-Sheng Lin, Shu Hu, Jinrong Hu, Xi Wu, Xin Wang

TL;DR

This work tackles medical question answering by introducing a six-phase, multi-agent framework that leverages the open-source Llama3.1:70B model in zero-shot mode. A novel case-generation module provides context-rich clinical cases to support decision-making, while expert voting ensures consensus and interpretability. Empirical results on the MedQA dataset show substantial gains in accuracy and macro metrics over Direct Inference and Chain-of-Thought baselines, with ablations confirming the importance of model scale and case generation. The proposed approach offers a scalable, explainable framework for clinical decision support that can be extended to broader medical reasoning tasks.

Abstract

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and performing complex reasoning. These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. Specifically, we leverage the Llama3.1:70B model, a state-of-the-art LLM, in a multi-agent architecture to enhance performance on the MedQA dataset using zero-shot learning. Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data. Experimental results show substantial performance gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model's interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.

LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

TL;DR

This work tackles medical question answering by introducing a six-phase, multi-agent framework that leverages the open-source Llama3.1:70B model in zero-shot mode. A novel case-generation module provides context-rich clinical cases to support decision-making, while expert voting ensures consensus and interpretability. Empirical results on the MedQA dataset show substantial gains in accuracy and macro metrics over Direct Inference and Chain-of-Thought baselines, with ablations confirming the importance of model scale and case generation. The proposed approach offers a scalable, explainable framework for clinical decision support that can be extended to broader medical reasoning tasks.

Abstract

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and performing complex reasoning. These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. Specifically, we leverage the Llama3.1:70B model, a state-of-the-art LLM, in a multi-agent architecture to enhance performance on the MedQA dataset using zero-shot learning. Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data. Experimental results show substantial performance gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model's interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.
Paper Structure (18 sections, 8 equations, 2 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 8 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs for Medical Problem Solving
  4. Multi-Agent Systems for Medical Decision-Making
  5. Methodology
  6. Multi-Agent Generation
  7. Proposition Analysis
  8. Case Generation
  9. Report Digest
  10. Voting Mechanism
  11. Decision Making
  12. Experiments
  13. Dataset and Evaluation Metric
  14. Experiments Settings
  15. Baselines
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Illustration of our proposed multi-agent architecture diagram, given a medical problem as an input to the larger model, which is divided into six phases: (1) Agent Generation, (2) Case Generation, (3) Proposition Analysis, (4) Report Digest, (5) Voting Mechanism, and (6) Decision Making.
  • Figure 2: Diagram of the Proposed Multi-Agent System Framework for Medical Question Answering