MedCoAct: Confidence-Aware Multi-Agent Collaboration for Complete Clinical Decision
It addresses the challenge of integrated clinical workflows for real-world healthcare, such as telemedicine, by enabling collaborative decision-making among AI agents.
The paper tackled the problem of isolated medical AI systems by proposing MedCoAct, a confidence-aware multi-agent framework that integrates specialized agents for diagnosis and medication decisions, achieving 67.58% accuracy in both tasks and outperforming single-agent systems by over 7%.
Autonomous agents utilizing Large Language Models (LLMs) have demonstrated remarkable capabilities in isolated medical tasks like diagnosis and image analysis, but struggle with integrated clinical workflows that connect diagnostic reasoning and medication decisions. We identify a core limitation: existing medical AI systems process tasks in isolation without the cross-validation and knowledge integration found in clinical teams, reducing their effectiveness in real-world healthcare scenarios. To transform the isolation paradigm into a collaborative approach, we propose MedCoAct, a confidence-aware multi-agent framework that simulates clinical collaboration by integrating specialized doctor and pharmacist agents, and present a benchmark, DrugCareQA, to evaluate medical AI capabilities in integrated diagnosis and treatment workflows. Our results demonstrate that MedCoAct achieves 67.58\% diagnostic accuracy and 67.58\% medication recommendation accuracy, outperforming single agent framework by 7.04\% and 7.08\% respectively. This collaborative approach generalizes well across diverse medical domains, proving especially effective for telemedicine consultations and routine clinical scenarios, while providing interpretable decision-making pathways.