AI Hospital: Interactive Evaluation and Collaboration of LLMs as Intern Doctors for Clinical Diagnosis
CoRR(2024)
摘要
The incorporation of Large Language Models (LLMs) in healthcare marks a
significant advancement. However, the application has predominantly been
limited to discriminative and question-answering tasks, which does not fully
leverage their interactive potential. To address this limitation, our paper
presents AI Hospital, a framework designed to build a real-time interactive
diagnosis environment. To simulate the procedure, we collect high-quality
medical records to create patient, examiner, and medical director agents. AI
Hospital is then utilized for the interactive evaluation and collaboration of
LLMs. Initially, we create a Multi-View Medical Evaluation (MVME) benchmark
where various LLMs serve as intern doctors for interactive diagnosis.
Subsequently, to improve diagnostic accuracy, we introduce a collaborative
mechanism that involves iterative discussions and a dispute resolution process
under the supervision of the medical director. In our experiments, we validate
the reliability of AI Hospital. The results not only explore the feasibility of
apply LLMs in clinical consultation but also confirm the effectiveness of the
dispute resolution focused collaboration method.
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