Summary of Review Of Multimodal Machine Learning Approaches in Healthcare, by Felix Krones et al.
Review of multimodal machine learning approaches in healthcare
by Felix Krones, Umar Marikkar, Guy Parsons, Adam Szmul, Adam Mahdi
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper reviews recent advances in machine learning approaches that integrate multiple sources of information for improved decision making in healthcare. By combining data from various modalities such as demographic information, laboratory data, vital signs, and imaging data, these methods can better replicate the clinical practice of contextualizing findings. The review discusses fusion techniques, existing multimodal datasets, and training strategies, with a focus on imaging data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how doctors use machine learning to make decisions by combining different types of patient information. Normally, machine learning only uses one type of data, but this paper shows that using multiple types can be really helpful. It talks about what kinds of data are used in healthcare, like lab tests and X-rays, and how researchers are developing new ways to combine all this information to make better decisions. |
Keywords
* Artificial intelligence * Machine learning
