Summary of Foundation Models in Radiology: What, How, When, Why and Why Not, by Magdalini Paschali et al.
Foundation Models in Radiology: What, How, When, Why and Why Not
by Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis Langlotz, Sergios Gatidis, Akshay Chaudhari
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper reviews recent advancements in artificial intelligence, specifically foundation models capable of processing both textual and imaging data. Foundation models are trained on vast amounts of unlabeled data, demonstrating high performance across various tasks. This review aims to standardize terminology around foundation models, focusing on training data requirements, model training paradigms, capabilities, and evaluation strategies for radiology applications. The authors outline potential pathways for training radiology-specific foundation models while highlighting benefits and challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are large-scale AI models that can process both text and images. These models are trained on huge amounts of data and do really well at many tasks. This paper is like a guidebook that explains what these models are, how they work, and why they’re important for radiology (the study of medical imaging). It also talks about the good things and the tricky things about using these models in medicine. |
