CheXstray: A Real-Time Multi-Modal Monitoring Workflow for Medical Imaging AI
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More recent works that seek to directly detect data drift in clinical settings are not systematic, often do not include examples of real-world drift, and still use decreases in performance measures to demonstrate utility.
“While more recent works also seek to directly detect data drift in clinical settings 19,27 , their explorations of data drift are not systematic, often do not include examples of real-world drift, and still use decreases in performance measures to demonstrate the utility of their methods.”
From Empirical data drift detection experiments on real-world medical imaging data (2024) · cites this paper as [19]
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Empirical data drift detection experiments on real-world medical imaging data
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- Authors
- Merkow, J., Soin, A., Long, J., Cohen, J. P., Saligrama, S., Bridge, C., Yang, X., Kaiser, S., Borg, S., Tarapov, I., & Lungren, M. P.
- Affiliations
- Microsoft (United States) (US)
- Stanford University (US)
- Harvard University (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Merkow, Jameson
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 326-336
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4387211755
- Cited by
- 13
- References
- 19
- Citations by year
- 2026: 6 · 2025: 2 · 2024: 5
- FWCI
- 2.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- model drift detection, medical imaging AI, real-time monitoring, variational autoencoder, DICOM metadata, chest radiography, pediatric pneumonia, CheXpert
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