Can multi-agent RL improve dynamic medical diagnosis?
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
This paper proposes an RL-driven multi-agent framework for dynamic medical diagnosis, addressing the limitations of current foundation models in handling multi-turn interactions and premature diagnostic conclusions. It simulates a clinical consultation with doctor, patient, and examiner agents. Crucially for LLM-based systems, it uses a hierarchical action set derived from clinical consultation flow and medical textbooks to guide the LLM's decision-making process, integrating multi-modal data into a textual representation for enhanced interpretability. This structured approach, combined with reinforcement learning, improves diagnostic accuracy and addresses premature closure, a common issue in both LLM and human diagnoses.