Summary of Inquire, Interact, and Integrate: a Proactive Agent Collaborative Framework For Zero-shot Multimodal Medical Reasoning, by Zishan Gu et al.
Inquire, Interact, and Integrate: A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning
by Zishan Gu, Fenglin Liu, Changchang Yin, Ping Zhang
First submitted to arxiv on: 19 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 proposed framework, MultiMedRes, addresses the limitations of large language models (LLMs) in healthcare by incorporating a learner agent that proactively gains domain-specific knowledge from expert models. This multimodal medical collaborative reasoning framework consists of three steps: Inquire, Interact, and Integrate. The learner agent decomposes complex medical problems into sub-problems, interacts with domain-specific expert models to acquire knowledge, and integrates the acquired information to solve the problem. The approach demonstrates state-of-the-art performance in difference visual question answering for X-ray images, outperforming fully supervised methods. This framework can be incorporated into various LLMs and multimodal LLMs to significantly boost their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI system helps doctors by using multiple sources of information to solve medical problems. The system is called MultiMedRes. It works by breaking down a complex problem into smaller parts, asking experts questions to get more information, and then combining that information to make a diagnosis. This approach does not need any training data, which makes it very useful. In one test, the system did better than other approaches at looking at X-ray images and identifying differences between them. |
Keywords
» Artificial intelligence » Question answering » Supervised
