Summary of A Comprehensive Survey Of Foundation Models in Medicine, by Wasif Khan et al.
A Comprehensive Survey of Foundation Models in Medicine
by Wasif Khan, Seowung Leem, Kyle B. See, Joshua K. Wong, Shaoting Zhang, Ruogu Fang
First submitted to arxiv on: 15 Jun 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 This paper presents a comprehensive review of foundation models (FMs) in medicine, focusing on their evolution, learning strategies, flagship models, applications, and challenges. FMs are large-scale deep learning models trained on massive datasets using self-supervised learning techniques. They have demonstrated remarkable success across multiple healthcare domains, including clinical natural language processing, medical image analysis, and omics research. The paper examines how prominent FMs like BERT and GPT families transform various aspects of healthcare. Additionally, it provides a detailed taxonomy of FM-enabled healthcare applications, highlighting open research questions and lessons learned to guide researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence (AI) models called foundation models are changing the way we understand medicine. These AI models are very good at learning from large amounts of data without being specifically taught what to do. This means they can help us in many areas of healthcare, such as understanding medical texts and images, and analyzing biological data. The paper looks at how these AI models have already helped us in different ways and what we still need to learn to make the most of them. |
Keywords
» Artificial intelligence » Bert » Deep learning » Gpt » Natural language processing » Self supervised
