Enhancing diagnostic capability with multi-agents conversational large language models.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40082662.
- Also identified by DOI 10.1038/s41746-025-01550-0 and PMC identifier 11906805.
- Licence recorded as CC BY-NC-ND.
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Abstract
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 outperformed other methods including Chain of Thoughts (CoT), Self-Refine, and Self-Consistency with higher performance and more output tokens. This framework significantly enhanced LLMs' diagnostic capabilities, effectively bridging theoretical knowledge and practical clinical application. Our findings highlight the potential of multi-agent LLMs in healthcare and suggest further research into their clinical implementation.