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（LLMs）在人类交互中展现出卓越能力，但其在医学领域的应用仍未得到充分探索。此前研究主要聚焦于通过考试评估医学知识掌握程度，这种模式与真实场景相去甚远，难以有效衡量LLMs在临床任务中的能力。为推进大语言模型在医疗领域的应用，本文提出自动交互式评估（AIE）框架与状态感知患者模拟器（SAPS），旨在弥合传统LLM评估与临床实践精细化需求之间的鸿沟。与依赖静态医学知识评估的既有方法不同，AIE和SAPS通过多轮医患模拟对话构建动态化、高仿真的评估平台。该方案不仅更贴近真实临床场景，还能深度解析LLMs应对复杂患者交互的行为模式。广泛实验验证表明，AIE框架的评估结果与人工评估高度吻合，凸显其革新医疗LLM测试范式、助力优化医疗服务的巨大潜力。