A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making

• URL: http://arxiv.org/abs/2411.00248v1
• Date: Thu, 31 Oct 2024 22:58:08 GMT
• Title: A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
• Authors: Yubin Kim, Chanwoo Park, Hyewon Jeong, Cristina Grau-Vilchez, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, Hae Won Park,
• Abstract summary: Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data. Our MDAgents address this need by dynamically assigning collaboration structures to LLMs based on task complexity. This framework improves diagnostic accuracy and supports adaptive responses in complex, real-world medical scenarios.
• Score: 44.42749802364181
• License:
• Abstract: 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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