
AI_Hospital
AI Hospital: Interactive Evaluation and Collaboration of LLMs as Intern Doctors for Clinical Diagnosis
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AI Hospital is a research repository focusing on the interactive evaluation and collaboration of Large Language Models (LLMs) as intern doctors for clinical diagnosis. The repository includes a simulation module tailored for various medical roles, introduces the Multi-View Medical Evaluation (MVME) Benchmark, provides dialog history documents of LLMs, replication instructions, performance evaluation, and guidance for creating intern doctor agents. The collaborative diagnosis with LLMs emphasizes dispute resolution. The study was authored by Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang, Zhongyu Wei, Jun Xie, Fei Huang, and Jingren Zhou.
README:
AI Hospital: Interactive Evaluation and Collaboration of Large Language Models as Intern Doctors for Clinical Diagnosis [Paper]
Welcome to the repository for our research paper, "AI Hospital: Interactive Evaluation and Collaboration of Large Language Models as Intern Doctors for Clinical Diagnosis." This repository hosts our primary simulation module tailored for various medical roles involved in the study.
To set up your environment, run the following command:
pip install -r requirements.txt
Our study introduces the MVME Benchmark for evaluating Large Language Models (LLMs) in the role of intern doctors for clinical diagnosis. We have developed our medical record dataset, available for review at our medical record dataset, sourced from iiyi.
You can find the dialog history documents of LLMs featured in our study at Dialog_History. Additionally, the one-step diagnostic reports are located in One-Step.
Navigate to the source directory:
cd ./src
Before running the script, open scripts/run.sh
and enter your API keys for the required services. For instance:
- For OpenAI Models (e.g., GPT-4):
OPENAI_API_KEY=""
,OPENAI_API_BASE=""
- For Alibaba Models (e.g., Qwen-Max):
DASHSCOPE_API_KEY=""
- For Wenxin Models (e.g., Wenxin-4.0):
WENXIN_API_KEY=""
,WENXIN_SECRET_KEY=""
Execute the script with:
bash scripts/run.sh
To assess the performance, navigate to the source directory and execute:
cd ./src
bash scripts/eval.sh
To develop your intern doctor agent, base your implementation on the Doctor class. Refer to GPTDoctor for agents requiring online API calls or HuatuoGPTDoctor for GPU-dependent agents.
Register your model in the initialization file within the agents directory.
Navigate to the source directory:
cd ./src
Open scripts/run_md.sh
and input your API keys for the necessary services as described above.
Execute the script with:
bash scripts/run_md.sh
This study was authored by Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang, Zhongyu Wei, Jun Xie, Fei Huang, and Jingren Zhou. We encourage the use of our code and data in your research and kindly request citation of our paper as follows:
@article{fan2024ai,
title={AI Hospital: Interactive Evaluation and Collaboration of LLMs as Intern Doctors for Clinical Diagnosis},
author={Fan, Zhihao and Tang, Jialong and Chen, Wei and Wang, Siyuan and Wei, Zhongyu and Xi, Jun and Huang, Fei and Zhou, Jingren},
journal={arXiv preprint arXiv:2402.09742},
year={2024}
}
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