Summary of Agent Hospital: a Simulacrum Of Hospital with Evolvable Medical Agents, by Junkai Li et al.
Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
by Junkai Li, Yunghwei Lai, Weitao Li, Jingyi Ren, Meng Zhang, Xinhui Kang, Siyu Wang, Peng Li, Ya-Qin Zhang, Weizhi Ma, Yang Liu
First submitted to arxiv on: 5 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in large language models (LLMs) have led to significant developments in medical artificial intelligence. These models, designed to process and generate human-like text, are being used by autonomous agents that can plan, reflect, and interact with the environment. We introduce Agent Hospital, a simulacrum of a hospital where patients, nurses, and doctors are all LLM-powered autonomous agents. Within this simulated environment, doctor agents evolve by treating a large number of patient agents without requiring manual labeling of training data. After processing tens of thousands of patient agents, these evolved doctor agents outperform state-of-the-art medical agent methods on the MedQA benchmark, which consists of US Medical Licensing Examination (USMLE) test questions. Our methods have the potential to benefit various applications beyond medical AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are helping artificial intelligence in medicine grow rapidly. These models can understand and create text like humans, but they’re also being used by machines that can make plans, think about things, and use tools. We created a simulated hospital called Agent Hospital where all the patients, nurses, and doctors are computer programs using these language models. In this fake hospital, doctor robots get smarter by treating many patient robots without needing human help to train them. After doing this for tens of thousands of patient robots, these smart doctor robots can do medical tasks better than other AI methods on a special test. |
