让大模型动态协作，提升医疗决策准确性和效率。
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
- 根据任务复杂度动态调整大模型协作方式，模拟真实临床协作。
- 在复杂医疗场景中提升诊断准确率，计算成本低于静态多代理方法。
- 适合临床辅助决策系统开发者及医疗AI研究者参考。
医疗决策（MDM）是一个多维度过程，需要临床医生评估复杂的多模态患者数据，常以协作方式进行。大语言模型（LLMs）有望通过整合海量医学知识和多模态健康数据来简化这一过程。然而，单智能体模型往往难以应对需要灵活、协作式解决问题的复杂医疗场景。本文提出MDAgents框架，根据任务复杂度动态分配大模型间的协作结构，模仿真实世界的临床协作与决策模式。该框架在复杂真实医疗场景中提升了诊断准确性，并支持自适应响应，同时在计算成本上优于静态多代理决策方法，为各类医疗环境中的临床医生提供了有价值的辅助工具。
原文摘要 · Abstract (English)
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data. However, single-agent are often ill-suited for nuanced medical contexts requiring adaptable, collaborative problem-solving. Our MDAgents addresses this need by dynamically assigning collaboration structures to LLMs based on task complexity, mimicking real-world clinical collaboration and decision-making. This framework improves diagnostic accuracy and supports adaptive responses in complex, real-world medical scenarios, making it a valuable tool for clinicians in various healthcare settings, and at the same time, being more efficient in terms of computing cost than static multi-agent decision making methods.
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