Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917963618058240 |
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| author | Wang, Sihan Jiang, Suiyang Gao, Yibo Wang, Boming Gao, Shangqi Zhuang, Xiahai |
| author_facet | Wang, Sihan Jiang, Suiyang Gao, Yibo Wang, Boming Gao, Shangqi Zhuang, Xiahai |
| contents | Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation models show promising potential for enhancing diagnosis combining multi-modal information. While these models excel in static tasks, they struggle with dynamic diagnosis, failing to manage multi-turn interactions and often making premature diagnostic decisions due to insufficient persistence in information collection.To address this, we propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process, integrating multiple clinical information for effective diagnosis. Our approach incorporates a hierarchical action set, structured from clinic consultation flow and medical textbook, to effectively guide the decision-making process. This strategy improves agent interactions, enabling them to adapt and optimize actions based on the dynamic state. We evaluated our framework on a public dynamic diagnosis benchmark. The proposed framework evidentially improves the baseline methods and achieves state-of-the-art performance compared to existing foundation model-based methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis Wang, Sihan Jiang, Suiyang Gao, Yibo Wang, Boming Gao, Shangqi Zhuang, Xiahai Artificial Intelligence Multiagent Systems Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation models show promising potential for enhancing diagnosis combining multi-modal information. While these models excel in static tasks, they struggle with dynamic diagnosis, failing to manage multi-turn interactions and often making premature diagnostic decisions due to insufficient persistence in information collection.To address this, we propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process, integrating multiple clinical information for effective diagnosis. Our approach incorporates a hierarchical action set, structured from clinic consultation flow and medical textbook, to effectively guide the decision-making process. This strategy improves agent interactions, enabling them to adapt and optimize actions based on the dynamic state. We evaluated our framework on a public dynamic diagnosis benchmark. The proposed framework evidentially improves the baseline methods and achieves state-of-the-art performance compared to existing foundation model-based methods. |
| title | Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2503.16547 |