AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
Artificial intelligence has significantly advanced healthcare, particularly through large language models (LLMs) that excel in medical question answering benchmarks. However, their real-world clinical application remains limited due to the complexities of doctor-patient interactions. To address this, we introduce AI Hospital, a multi-agent framework simulating dynamic medical interactions between Doctor as player and NPCs including Patient, Examiner, Chief Physician. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation (MVME) benchmark, utilizing high-quality Chinese medical records and NPCs to evaluate LLMs' performance in symptom collection, examination recommendations, and diagnoses. Additionally, a dispute resolution collaborative mechanism is proposed to enhance diagnostic accuracy through iterative discussions. Despite improvements, current LLMs exhibit significant performance gaps in multi-turn interactions compared to one-step approaches. Our findings highlight the need for further research to bridge these gaps and improve LLMs' clinical diagnostic capabilities. Our data, code, and experimental results are all open-sourced at urlhttps://github.com/LibertFan/AIHospital.
Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 30 of its 59 references that are in the map. First posted on arXiv 15 Feb 2024.
Builds on
30 of 59 references.
References · 30
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