In this paper, we introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness. All patients, nurses, and doctors are autonomous agents powered by large language models (LLMs). Our central goal is to enable a doctor agent to learn how to treat illness within the simulacrum. To do so, we propose a method called MedAgent-Zero. As the simulacrum can simulate disease onset and progression based on knowledge bases and LLMs, doctor agents can keep accumulating experience from both successful and unsuccessful cases. Simulation experiments show that the treatment performance of doctor agents consistently improves on various tasks. More interestingly, the knowledge the doctor agents have acquired in Agent Hospital is applicable to real-world medicare benchmarks. After treating around ten thousand patients (real-world doctors may take over two years), the evolved doctor agent achieves a state-of-the-art accuracy of 93.06% on a subset of the MedQA dataset that covers major respiratory diseases. This work paves the way for advancing the applications of LLM-powered agent techniques in medical scenarios.

该研究介绍了一个名为Agent Hospital的医院模拟系统，使用大型语言模型（LLMs）驱动的自主智能代理来治疗疾病，并通过一种名为MedAgent-Zero的方法使医生代理学会治疗疾病。实验结果显示，医生代理的治疗表现在各项任务上持续提高，并且在真实医疗基准数据集上取得了93.06%的准确率，为LLM驱动的代理技术在医疗场景中的应用提供了奠定基础。

Agent Hospital: 可进化的医学代理的医院模拟