Summary of Has Multimodal Learning Delivered Universal Intelligence in Healthcare? a Comprehensive Survey, by Qika Lin et al.
Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey
by Qika Lin, Yifan Zhu, Xin Mei, Ling Huang, Jingying Ma, Kai He, Zhen Peng, Erik Cambria, Mengling Feng
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores the development of artificial intelligence (AI) in intelligent healthcare and medicine, with a focus on multimodal learning. The authors analyze the current state of medical multimodal learning from three unique viewpoints: datasets, task-oriented methods, and universal foundation models. They discuss the potential impacts of advanced techniques in healthcare, including data and technologies, performance, and ethics. The findings suggest that current AI technologies have not yet achieved universal intelligence in healthcare, highlighting a significant journey ahead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence is changing healthcare and medicine. Researchers are using something called multimodal learning to make AI better. They’re studying what’s been done so far and thinking about what needs to be improved. The big question is: has AI reached a point where it can help everyone in healthcare? The answer is no, there’s still much work to be done. The authors also suggest 10 ways that researchers could improve AI for healthcare. |
