Abstract
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.
Abstract (translated)
在本文中，我们提出了一个名为Agent Hospital的医院模拟模型，该模型模拟了治疗疾病的过程。所有患者、护士和医生都是由大型语言模型（LLMs）驱动的自主代理。我们的核心目标是让医生代理学会在模拟中如何治疗疾病。为此，我们提出了一个名为MedAgent-Zero的方法。 由于模拟可以根据知识库和LLMs模拟疾病的发生和进展，医生代理可以从成功和失败案例中积累经验。仿真实验表明，医生代理在各种任务上的治疗效果不断提高。更令人兴奋的是，医生代理在Agent Hospital中所获得的知识可以应用于现实世界的医疗基准。 在治疗大约10,000名患者（现实世界的医生可能需要两年多才能完成）后，进化的医生代理在覆盖主要呼吸疾病的部分MedQA数据集上达到93.06%的准确率，为LLM-驱动代理技术在医疗场景中的应用铺平道路。
URL
https://arxiv.org/abs/2405.02957
