Summary of The Metric-framework For Assessing Data Quality For Trustworthy Ai in Medicine: a Systematic Review, by Daniel Schwabe et al.
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review
by Daniel Schwabe, Katinka Becker, Martin Seyferth, Andreas Klaß, Tobias Schäffter
First submitted to arxiv on: 21 Feb 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 The proposed METRIC-framework aims to establish a trustworthy AI system in medicine by ensuring data quality in machine learning applications. The framework focuses on 15 awareness dimensions, which developers should consider when assessing medical training datasets. This approach can help reduce biases, increase robustness, and facilitate interpretability, ultimately leading to more reliable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical AI products are being developed to revolutionize healthcare, but ensuring their trustworthiness is crucial. To achieve this, researchers must focus on data quality, as poor-quality data can lead to biased or unfair outcomes. A new framework, METRIC, has been proposed to address these concerns. By assessing medical training datasets using 15 awareness dimensions, developers can create more reliable and interpretable AI systems. |
Keywords
* Artificial intelligence * Machine learning
