Summary of Opportunities and Challenges in the Application Of Large Artificial Intelligence Models in Radiology, by Liangrui Pan et al.
Opportunities and challenges in the application of large artificial intelligence models in radiology
by Liangrui Pan, Zhenyu Zhao, Ying Lu, Kewei Tang, Liyong Fu, Qingchun Liang, Shaoliang Peng
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Large AI models have revolutionized various fields, including radiology imaging. This paper provides an overview of the development history and technical details of multimodal and video generation large models, as well as their applications in radiology education, report generation, and unimodal/multimodal radiology. The authors also highlight the challenges of using large AI models in radiology, aiming to accelerate innovation in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large AI models have changed the way we approach many tasks, including radiology imaging. This paper explains how these models work and their uses in education, report writing, and other areas of radiology. It also discusses some challenges that come with using these powerful tools, but ultimately shows how they can help us make progress in this important field. |
