MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks
Journal:
arXiv
Published Date:
May 18, 2025
Abstract
The rapid advancement of Large Language Models (LLMs) has stimulated interest
in multi-agent collaboration for addressing complex medical tasks. However, the
practical advantages of multi-agent collaboration approaches remain
insufficiently understood. Existing evaluations often lack generalizability,
failing to cover diverse tasks reflective of real-world clinical practice, and
frequently omit rigorous comparisons against both single-LLM-based and
established conventional methods. To address this critical gap, we introduce
MedAgentBoard, a comprehensive benchmark for the systematic evaluation of
multi-agent collaboration, single-LLM, and conventional approaches.
MedAgentBoard encompasses four diverse medical task categories: (1) medical
(visual) question answering, (2) lay summary generation, (3) structured
Electronic Health Record (EHR) predictive modeling, and (4) clinical workflow
automation, across text, medical images, and structured EHR data. Our extensive
experiments reveal a nuanced landscape: while multi-agent collaboration
demonstrates benefits in specific scenarios, such as enhancing task
completeness in clinical workflow automation, it does not consistently
outperform advanced single LLMs (e.g., in textual medical QA) or, critically,
specialized conventional methods that generally maintain better performance in
tasks like medical VQA and EHR-based prediction. MedAgentBoard offers a vital
resource and actionable insights, emphasizing the necessity of a task-specific,
evidence-based approach to selecting and developing AI solutions in medicine.
It underscores that the inherent complexity and overhead of multi-agent
collaboration must be carefully weighed against tangible performance gains. All
code, datasets, detailed prompts, and experimental results are open-sourced at
https://medagentboard.netlify.app/.
