A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929597639032832 |
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| author | Kim, Yubin Park, Chanwoo Jeong, Hyewon Grau-Vilchez, Cristina Chan, Yik Siu Xu, Xuhai McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won |
| author_facet | Kim, Yubin Park, Chanwoo Jeong, Hyewon Grau-Vilchez, Cristina Chan, Yik Siu Xu, Xuhai McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00248 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making Kim, Yubin Park, Chanwoo Jeong, Hyewon Grau-Vilchez, Cristina Chan, Yik Siu Xu, Xuhai McDuff, Daniel Lee, Hyeonhoon Breazeal, Cynthia Park, Hae Won Computation and Language 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. |
| title | A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.00248 |