Summary of Out-of-distribution Detection and Data Drift Monitoring Using Statistical Process Control, by Ghada Zamzmi et al.
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
by Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan, Paul H. Yi, Berkman Sahiner, Jana G. Delfino
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 Machine learning models are known to struggle when faced with new data that differs significantly from the training set. In clinical settings, this can have serious consequences, particularly if ML-enabled devices are used to support patient care. This paper explores ways to address this issue, which is critical for ensuring the safe and effective use of ML in healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models don’t work well when they encounter new data that’s very different from what they were trained on. This can be a big problem in hospitals where machines are used to help doctors and nurses take care of patients. The goal of this research is to find ways to fix this issue so that ML technology can be trusted to make good decisions in these situations. |
Keywords
* Artificial intelligence * Machine learning
