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systematic review of healthcare ML (2025)
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: ScienceDirect
This 2025 systematic review is relevant to AI safety practitioners interested in how robustness and generalization failures manifest in high-stakes real-world deployments, offering empirical grounding for theoretical safety concerns.
Metadata
Importance: 52/100journal articleanalysis
Summary
A systematic review examining the deployment and reliability of machine learning models in healthcare settings, focusing on issues of robustness, generalization, and safety across clinical applications. The review likely identifies common failure modes and gaps between model performance in research versus real-world clinical environments.
Key Points
- •Surveys the landscape of ML applications in healthcare, assessing how well models generalize across different patient populations and clinical settings.
- •Identifies robustness challenges including distribution shift, dataset bias, and poor performance on underrepresented groups.
- •Highlights the gap between benchmark performance and real-world deployment reliability in clinical ML systems.
- •Reviews evaluation methodologies and calls for more rigorous standards for validating healthcare ML before deployment.
- •Relevant to AI safety discussions around high-stakes deployment contexts where model failures have direct human consequences.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Distributional Shift | Risk | 91.0 |
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## Article preview
- [Abstract](https://www.sciencedirect.com/science/article/abs/pii/S1532046425001315#preview-section-abstract)
- [Introduction](https://www.sciencedirect.com/science/article/abs/pii/S1532046425001315#preview-section-introduction)
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[](https://www.sciencedirect.com/journal/journal-of-biomedical-informatics "Go to Journal of Biomedical Informatics on ScienceDirect")
## [Journal of Biomedical Informatics](https://www.sciencedirect.com/journal/journal-of-biomedical-informatics "Go to Journal of Biomedical Informatics on ScienceDirect")
[Volume 170](https://www.sciencedirect.com/journal/journal-of-biomedical-informatics/vol/170/suppl/C "Go to table of contents for this volume/issue"), October 2025, 104902
[](https://www.sciencedirect.com/journal/journal-of-biomedical-informatics/vol/170/suppl/C)
# Strategies for detecting and mitigating dataset shift in machine learning for health predictions: A systematic review
Author links open overlay panelGabriel Ferreira dos SantosSilvaa, Fabiano NovaesBarcellos Filhoa, Roberta MoreiraWichmannbc, Francisco Costada Silva Juniorc, Alexandre Dias PortoChiavegatto Filhoa
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[https://doi.org/10.1016/j.jbi.2025.104902](https://doi.org/10.1016/j.jbi.2025.104902 "Persistent link using digital object identifier") [Get rights and content](https://s100.copyright.com/AppDispatchServlet?publisherName=ELS&contentID=S1532046425001315&orderBeanReset=true)
## Abstract
### Objective
This review aims to provide a comprehensive overview of the literature on methods and techniques for identifying and correcting dataset shift in machine learning (ML) applications for health predictions.
### Methods
A systematic search was conducted across PubMed, IEEE Xplore, Scop
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