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

Abstract

Accurate and efficient question-answering systems are essential for high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they still face challenges in medical question answering, particularly in understanding domain-specific terminology and performing complex reasoning, limiting their effectiveness in critical applications. To address this, we propose a multi-agent medical question-answering (MedQA) system incorporating similar case generation. We leverage the Llama3.1:70B model in a multi-agent architecture to enhance enhance zero-shot classification on the MedQA dataset, utilizing the model’s inherent medical knowledge and reasoning capabilities without additional training data. Experimental results show substantial 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.

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