Enhancing diagnostic capability with multi-agents conversational large language models

Large Language Models (LLMs) show promise in healthcare tasks but face challenges in complex medical scenarios. We developed a Multi-Agent Conversation (MAC) framework for disease diagnosis, inspired by clinical Multi-Disciplinary Team discussions. Using 302 rare disease cases, we evaluated GPT-3.5, GPT-4, and MAC on medical knowledge and clinical reasoning. MAC outperformed single models in both primary and follow-up consultations, achieving higher accuracy in diagnoses and suggested tests. Optimal performance was achieved with four doctor agents and a supervisor agent, using GPT-4 as the base model. MAC demonstrated high consistency across repeated runs. Further comparative analysis showed MAC also…