Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916569239519232 |
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| author | Li, Junkai Lai, Yunghwei Li, Weitao Ren, Jingyi Zhang, Meng Kang, Xinhui Wang, Siyu Li, Peng Zhang, Ya-Qin Ma, Weizhi Liu, Yang |
| author_facet | Li, Junkai Lai, Yunghwei Li, Weitao Ren, Jingyi Zhang, Meng Kang, Xinhui Wang, Siyu Li, Peng Zhang, Ya-Qin Ma, Weizhi Liu, Yang |
| contents | The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI). While LLMs are designed to understand and generate text like a human, autonomous agents that utilize LLMs as their "brain" have exhibited capabilities beyond text processing such as planning, reflection, and using tools by enabling their "bodies" to interact with the environment. We introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness, in which all patients, nurses, and doctors are LLM-powered autonomous agents. Within the simulacrum, doctor agents are able to evolve by treating a large number of patient agents without the need to label training data manually. After treating tens of thousands of patient agents in the simulacrum (human doctors may take several years in the real world), the evolved doctor agents outperform state-of-the-art medical agent methods on the MedQA benchmark comprising US Medical Licensing Examination (USMLE) test questions. Our methods of simulacrum construction and agent evolution have the potential in benefiting a broad range of applications beyond medical AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents Li, Junkai Lai, Yunghwei Li, Weitao Ren, Jingyi Zhang, Meng Kang, Xinhui Wang, Siyu Li, Peng Zhang, Ya-Qin Ma, Weizhi Liu, Yang Artificial Intelligence The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI). While LLMs are designed to understand and generate text like a human, autonomous agents that utilize LLMs as their "brain" have exhibited capabilities beyond text processing such as planning, reflection, and using tools by enabling their "bodies" to interact with the environment. We introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness, in which all patients, nurses, and doctors are LLM-powered autonomous agents. Within the simulacrum, doctor agents are able to evolve by treating a large number of patient agents without the need to label training data manually. After treating tens of thousands of patient agents in the simulacrum (human doctors may take several years in the real world), the evolved doctor agents outperform state-of-the-art medical agent methods on the MedQA benchmark comprising US Medical Licensing Examination (USMLE) test questions. Our methods of simulacrum construction and agent evolution have the potential in benefiting a broad range of applications beyond medical AI. |
| title | Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.02957 |