Summary of Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions, by Kai Sun et al.
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
by Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun, Yu Luo, Ling Wang, Siyuan Wang, Na Guo, Lei Liu, Tian Zhao, Xinzhou Wang, Lei Yang, Shuo Jin, Jun Yan, Jiahong Dong
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper reviews recent developments in Medical Multimodal Foundation Models (MMFMs), which have revolutionized clinical diagnosis and treatment. MMFMs, known for their generalization capabilities and representational power, are being adapted to address various clinical tasks, from early diagnosis to personalized treatment strategies. The review focuses on datasets, model architectures, and clinical applications, exploring the challenges and opportunities in optimizing multimodal representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep learning is changing healthcare. It reviews a type of artificial intelligence called Medical Multimodal Foundation Models (MMFMs). These models are good at understanding different types of medical data, like images and texts. They can be used to help doctors diagnose patients and choose the best treatments. The paper talks about what kind of data MMFMs use, how they’re built, and how they’re helping in healthcare. |
Keywords
» Artificial intelligence » Deep learning » Generalization
