  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.
