Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis

• URL: http://arxiv.org/abs/2503.16547v1
• Date: Wed, 19 Mar 2025 08:47:18 GMT
• Title: Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
• Authors: Sihan Wang, Suiyang Jiang, Yibo Gao, Boming Wang, Shangqi Gao, Xiahai Zhuang,
• Abstract summary: We propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process.<n>Our approach incorporates a hierarchical action set, structured from clinic consultation flow and medical textbook, to effectively guide the decision-making process.<n>This strategy improves agent interactions, enabling them to adapt and optimize actions based on the dynamic state.
• Score: 20.59719567178192
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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