Summary of Mdagents: An Adaptive Collaboration Of Llms For Medical Decision-making, by Yubin Kim et al.
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
by Yubin Kim, Chanwoo Park, Hyewon Jeong, Yik Siu Chan, Xuhai Xu, Daniel McDuff, Hyeonhoon Lee, Marzyeh Ghassemi, Cynthia Breazeal, Hae Won Park
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medical Decision-making Agents (MDAgents) is a novel framework that leverages Large Language Models (LLMs) to tackle complex medical tasks. By automatically assigning a collaboration structure to teams of LLMs, MDAgents emulate real-world medical decision-making processes. The framework outperforms baseline methods in seven out of ten benchmarks on tasks requiring medical knowledge and multi-modal reasoning, with significant improvements up to 4.2% (p < 0.05). Ablation studies reveal that MDAgents effectively determine medical complexity to optimize efficiency and accuracy across diverse medical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MDAgents is a new way to use big language models in medicine. It helps teams of these models work together better, just like doctors do when making decisions. The system did really well on tests with real-world medical information, beating other methods most of the time. This shows that MDAgents can help make more accurate and efficient medical decisions. |
Keywords
» Artificial intelligence » Multi modal
