Summary of Veridical Data Science For Medical Foundation Models, by Ahmed Alaa et al.
Veridical Data Science for Medical Foundation Models
by Ahmed Alaa, Bin Yu
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 impact of foundation models (FMs) on the standard data science workflow in medicine. Specifically, it discusses how large language models (LLMs) are changing the way medical professionals approach data analysis, shifting from specialized predictive models to generalist FMs pre-trained on vast amounts of unstructured data. This new workflow, known as the foundation model lifecycle (FMLC), involves distinct upstream and downstream processes, with computational resources, model and data access, and decision-making power distributed among multiple stakeholders. The paper examines how this shift challenges the principles of Veridical Data Science (VDS), including predictability, computability, and stability (PCS). It proposes recommendations for a reimagined medical FMLC that expands and refines these PCS principles for VDS, considering the computational and accessibility constraints inherent to FMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FMs are changing how doctors analyze data. Right now, they’re using big language models (LLMs) to look at lots of unorganized information. This is different from before, when they used specialized computers to answer specific questions. Now, these LLMs can be adapted for many medical tasks and problems. But this new way of working is causing some issues with the way we think about data science. It’s like a puzzle that doesn’t quite fit together. The paper looks at what’s going on and suggests ways to make it work better. |
