Summary of Mmedagent: Learning to Use Medical Tools with Multi-modal Agent, by Binxu Li et al.
MMedAgent: Learning to Use Medical Tools with Multi-modal Agent
by Binxu Li, Tiankai Yan, Yuanting Pan, Jie Luo, Ruiyang Ji, Jiayuan Ding, Zhe Xu, Shilong Liu, Haoyu Dong, Zihao Lin, Yixin Wang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces Multi-modal Medical Agent (MMedAgent), a large language model-based agent designed for the medical field. Despite the success of multi-modal large language models (MLLMs), they often fall short when compared to specialized models. MMedAgent is the first agent specifically designed for the medical domain, curating an instruction-tuning dataset comprising six medical tools solving seven tasks across five modalities. The agent chooses the most suitable tools for a given task, achieving superior performance compared to state-of-the-art open-source methods and even the closed-source model GPT-4o. MMedAgent also exhibits efficiency in updating and integrating new medical tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates an AI that helps doctors with their work by choosing the right tools for the job. Right now, these tools are limited and don’t always get it right. The AI is called MMedAgent and it’s special because it was designed just for medicine. It learned how to use different tools for different tasks by looking at what other people have done before. This helps MMedAgent do its job better than others. The best part is that it can also learn new things and get even better over time. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Large language model » Multi modal
