Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
Journal:
arXiv
Published Date:
Mar 19, 2025
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.
