Strategies for detecting and mitigating dataset shift in machine learning for health predictions: A systematic review

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, Scopus, and Web of Science, targeting articles published between January 1, 2019, and March 15, 2025. earch strings combined terms related to machine learning, healthcare, and dataset shift. A total of 32 studies were included, and were evaluated based on dataset shift types addressed, detection and correction strategies used, algorithmic choices, and reported impacts on model performance.

Results

The review identified a wide range of dataset shift types, with temporal shift and concept drift being the most commonly addressed. Model-based monitoring and statistical tests were the most frequent detection strategies, while retraining and feature engineering were the predominant correction approaches. Most methods demonstrate moderate interpretability, computational feasibility, and generalizability. However, a lack of standardized performance metrics and external validations limited the comparability of results across studies.

Conclusion

While several promising approaches for managing dataset shift in health-related ML models have been proposed, no single method emerged as broadly generalizable across use cases. The implementation of these techniques in real-world clinical workflows remains limited. Future research should prioritize prospective evaluations, subgroup-specific analyses (e.g., by race, age, or geographic region), and integration into clinical decision-support systems to ensure robust and equitable ML deployment in healthcare settings. A structured summary table and conceptual pipeline diagram are provided to support practical adoption.