Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored. Previous works mainly focus on the performance of medical knowledge with examinations, which is far from the realistic scenarios, falling short in assessing the abilities of LLMs on clinical tasks. In the quest to enhance the application of Large Language Models (LLMs) in healthcare, this paper introduces the Automated Interactive Evaluation (AIE) framework and the State-Aware Patient Simulator (SAPS), targeting the gap between traditional LLM evaluations and the nuanced demands of clinical practice. Unlike prior methods that rely on static medical knowledge assessments, AIE and SAPS provide a dynamic, realistic platform for assessing LLMs through multi-turn doctor-patient simulations. This approach offers a closer approximation to real clinical scenarios and allows for a detailed analysis of LLM behaviors in response to complex patient interactions. Our extensive experimental validation demonstrates the effectiveness of the AIE framework, with outcomes that align well with human evaluations, underscoring its potential to revolutionize medical LLM testing for improved healthcare delivery.

翻译：大语言模型在人类交互中展现出卓越的能力，但其在医学领域的应用仍有待深入探索。先前的研究主要聚焦于通过考试评估医学知识掌握程度，这与实际场景相去甚远，未能充分评估大语言模型在临床任务中的能力。为促进大语言模型在医疗健康领域的应用，本文提出自动交互式评估框架与状态感知患者模拟器，旨在填补传统评估方法与临床实践精细需求之间的鸿沟。与依赖静态医学知识评估的既有方法不同，该框架与模拟器通过多轮医患模拟对话，构建动态、逼真的评估平台。该方案不仅更贴近真实临床场景，还能详细分析大语言模型面对复杂患者交互时的行为表现。广泛实验验证表明，自动交互式评估框架效果显著，评估结果与人工评价高度吻合，凸显其在革新医疗大语言模型测试、改善医疗服务中的巨大潜力。