MedGen: An Explainable Multi-Agent Architecture for Clinical Decision Support through Multisource Knowledge Fusion

Agents in medical decision support have been extensively researched, particularly in areas like evidence support, multimorbidity management, and patient-specific needs. However, current approaches lack a unified method to address real-time evidence updates, manage multiple diseases independently, and incorporate personalized patient needs. Existing Large Language Model (LLM) agents are limited by their reliance on static knowledge bases, hindering their ability to promptly update clinical guidelines and meet diverse patient requirements. Moreover, the interpretability of LLMs remains a significant concern, leading to skepticism in their medical application. To address these challenges, we developed MedGen, a multi-agent architecture that decomposes the clinical decision-making process into stages such as clinical goal setting, data collection, argumentation linking, and plan selection. This structured approach allows LLM agents to provide both reasoning evidence and a transparent reasoning process, enhancing the reliability and interpretability of outcomes. Finally, a case study of breast cancer and depression is combined to illustrate our architecture.