@inproceedings{li-etal-2024-mmedagent,
title = "{MM}ed{A}gent: Learning to Use Medical Tools with Multi-modal Agent",
author = "Li, Binxu and
Yan, Tiankai and
Pan, Yuanting and
Luo, Jie and
Ji, Ruiyang and
Ding, Jiayuan and
Xu, Zhe and
Liu, Shilong and
Dong, Haoyu and
Lin, Zihao and
Wang, Yixin",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.510/",
doi = "10.18653/v1/2024.findings-emnlp.510",
pages = "8745--8760",
abstract = "Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited generality and often fall short when compared to specialized models. Recently, LLM-based agents have been developed to address these challenges by selecting appropriate specialized models as tools based on user inputs. However, such advancements have not been extensively explored within the medical domain. To bridge this gap, this paper introduces the first agent explicitly designed for the medical field, named \textbf{M}ulti-modal \textbf{Med}ical \textbf{Agent} (MMedAgent). We curate an instruction-tuning dataset comprising six medical tools solving seven tasks across five modalities, enabling the agent to choose the most suitable tools for a given task. Comprehensive experiments demonstrate that MMedAgent achieves superior performance across a variety of medical tasks compared to state-of-the-art open-source methods and even the closed-source model, GPT-4o. Furthermore, MMedAgent exhibits efficiency in updating and integrating new medical tools."
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<abstract>Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited generality and often fall short when compared to specialized models. Recently, LLM-based agents have been developed to address these challenges by selecting appropriate specialized models as tools based on user inputs. However, such advancements have not been extensively explored within the medical domain. To bridge this gap, this paper introduces the first agent explicitly designed for the medical field, named Multi-modal Medical Agent (MMedAgent). We curate an instruction-tuning dataset comprising six medical tools solving seven tasks across five modalities, enabling the agent to choose the most suitable tools for a given task. Comprehensive experiments demonstrate that MMedAgent achieves superior performance across a variety of medical tasks compared to state-of-the-art open-source methods and even the closed-source model, GPT-4o. Furthermore, MMedAgent exhibits efficiency in updating and integrating new medical tools.</abstract>
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%0 Conference Proceedings
%T MMedAgent: Learning to Use Medical Tools with Multi-modal Agent
%A Li, Binxu
%A Yan, Tiankai
%A Pan, Yuanting
%A Luo, Jie
%A Ji, Ruiyang
%A Ding, Jiayuan
%A Xu, Zhe
%A Liu, Shilong
%A Dong, Haoyu
%A Lin, Zihao
%A Wang, Yixin
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-mmedagent
%X Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited generality and often fall short when compared to specialized models. Recently, LLM-based agents have been developed to address these challenges by selecting appropriate specialized models as tools based on user inputs. However, such advancements have not been extensively explored within the medical domain. To bridge this gap, this paper introduces the first agent explicitly designed for the medical field, named Multi-modal Medical Agent (MMedAgent). We curate an instruction-tuning dataset comprising six medical tools solving seven tasks across five modalities, enabling the agent to choose the most suitable tools for a given task. Comprehensive experiments demonstrate that MMedAgent achieves superior performance across a variety of medical tasks compared to state-of-the-art open-source methods and even the closed-source model, GPT-4o. Furthermore, MMedAgent exhibits efficiency in updating and integrating new medical tools.
%R 10.18653/v1/2024.findings-emnlp.510
%U https://aclanthology.org/2024.findings-emnlp.510/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.510
%P 8745-8760
Markdown (Informal)
[MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://aclanthology.org/2024.findings-emnlp.510/) (Li et al., Findings 2024)
ACL
- Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, and Yixin Wang. 2024. MMedAgent: Learning to Use Medical Tools with Multi-modal Agent. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8745–8760, Miami, Florida, USA. Association for Computational Linguistics.