MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Yubin Kim1, Chanwoo Park1, Hyewon Jeong1, Yik Siu Chan1, Xuhai Xu1, Daniel McDuff2, Hyeonhoon Lee3, Marzyeh Ghassemi1, Cynthia Breazeal1, Hae Won Park1,
1Massachusetts Institute of Technology 2Google Research 3Seoul National University Hospital
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🎉 News: Our paper has been accepted as an oral paper at NeurIPS 2024!

Main Figure AgentClinic

MDAgents is a framework that adapts the collaboration of LLMs for complex medical decision-making, improving performance on major medical benchmarks.

Abstract

Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open question. We introduce a novel multi-actor framework, named MDAgents that helps to address this gap by automatically assigning a collaboration structure to a team of LLMs. The assigned solo or group collaboration structure is tailored to the medical task at hand, a simple emulation inspired by the way real-world medical decision-making processes are adapted to tasks of different complexities. We evaluate our framework and baseline methods using state-of-the-art LLMs across a suite of real-world medical knowledge and clinical diagnosis benchmarks. MDAgents achieved the best performance in seven out of ten benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant improvement of up to 6.5% (p < 0.05) compared to previous methods' best performances. Ablation studies reveal that MDAgents effectively determines medical complexity to optimize for efficiency and accuracy across diverse medical tasks. Notably, the combination of moderator review and external medical knowledge in group collaboration resulted in an average accuracy improvement of 11.8%. Our code can be found at https://github.com/mitmedialab/MDAgents.

Main Result

Our method outperforms Solo and Group settings across different medical benchmarks. MDAgents significantly outperforms (p < 0.05) both Solo and Group setting methods, showing best performance in 7 out of 10 medical benchmarks tested. This reveals the effectiveness of adaptive strategies integrated within our system, particularly when navigating through the text-only (e.g., DDXPlus where it outperformed the best performance of single-agent by 7.2% and multi-agent by 9.5%) and text-image datasets (e.g., Path-VQA, PMC-VQA and MIMIC-CXR). Our approach not only comprehends textual information with high precision but also adeptly synthesizes visual data, a pivotal capability in medical diagnostic evaluations.

Bias Figure AgentClinic

Case Study

The design of MDAgents incorporates four stages: 1) Medical Complexity Check - The system evaluates the medical query, categorizing it as low, moderate, or high complexity based on clinical decision-making techniques. 2) Expert Recruitment - Based on complexity, the framework activates a single Primary Care Clinician (PCC) for low complexity issues, or a Multi-disciplinary Team (MDT) or Integrated Care Team (ICT) for moderate or high complexities. 3) Analysis and Synthesis - Solo queries use prompting techniques like Chain-of-Thought (CoT) and Self-Consistency (SC). MDTs involve multiple LLM agents forming a consensus, while ICTs synthesize information for the most complex cases. 4) Decision-making - The final stage synthesizes all inputs to provide a well-informed answer to the medical query.

Bias Figure AgentClinic

BibTeX

@misc{kim2024mdagentsadaptivecollaborationllms,
      title={MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making}, 
      author={Yubin Kim and Chanwoo Park and Hyewon Jeong and Yik Siu Chan and Xuhai Xu and Daniel McDuff and Hyeonhoon Lee and Marzyeh Ghassemi and Cynthia Breazeal and Hae Won Park},
      year={2024},
      eprint={2404.15155},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2404.15155}
}