Summary of Data-centric Foundation Models in Computational Healthcare: a Survey, by Yunkun Zhang et al.
Data-Centric Foundation Models in Computational Healthcare: A Survey
by Yunkun Zhang, Jin Gao, Zheling Tan, Lingfeng Zhou, Kexin Ding, Mu Zhou, Shaoting Zhang, Dequan Wang
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the potential of foundation models (FMs) in computational healthcare. The authors highlight how FMs, driven by pre-training data and human instructions, have created a data-centric AI paradigm that prioritizes high-quality data characterization, quality, and scale. They discuss various data-centric approaches in the FM era, from model pre-training to inference, aimed at improving healthcare workflows. The paper also explores key perspectives on AI security, assessment, and alignment with human values. Finally, it offers a promising outlook for FM-based analytics enhancing patient outcome and clinical workflow performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks into how foundation models can help in healthcare. Foundation models are special kinds of artificial intelligence that use pre-trained data and instructions from humans to improve their abilities. The authors think that these models can make healthcare better by processing large amounts of high-quality data, which is a big problem right now. They explore different ways to use these models to make healthcare workflows more efficient. They also talk about how to keep AI safe and align it with human values. Overall, the paper thinks that using foundation models in healthcare could lead to better patient outcomes and improved clinical workflows. |
Keywords
* Artificial intelligence * Alignment * Inference
