MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks

2025-05-18

AI Summary

Existing medical AI evaluation lacks multimodal, multitask benchmarks grounded in real-world clinical settings and fails to enable fair, unified comparisons across multi-agent systems, single large language models (LLMs), and conventional methods. Method: We introduce the first clinical-realism-driven, multimodal, multi-agent collaborative benchmark for healthcare, encompassing four task categories: visual question answering, patient summary generation, electronic health record (EHR) predictive modeling, and clinical workflow automation. We establish standardized prompting, reproducible experimental protocols, and an open-source evaluation framework to enable rigorous cross-method comparison. Results: Multi-agent systems significantly improve task completeness only in clinical workflow automation; they underperform mature traditional methods on all other tasks. This highlights the critical need for task-driven AI methodology selection. All code, datasets, and evaluation results are publicly released to foster transparency and reproducibility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

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/.

Problem

• Evaluating multi-agent collaboration in medical tasks
• Comparing multi-agent with single-LLM and conventional methods
• Assessing performance across diverse clinical scenarios

Innovation

• Introduces MedAgentBoard for multi-agent collaboration benchmarking
• Evaluates diverse medical tasks across text and images
• Compares multi-agent with single-LLM and conventional methods