Exploring Robustness in Healthcare Machine Learning Models: A Comprehensive Review
The importance of robust machine learning models in healthcare cannot be overstated. Ensuring these models perform reliably under various conditions is crucial for their successful deployment and acceptance. This article delves into the multifaceted concept of robustness, examining its implications across different types of data and predictive models used in healthcare applications.
Machine learning models in healthcare face numerous challenges that can affect their performance, such as variations in input data, missing information, and label noise. The review identified eight key concepts of robustness, each addressing specific perturbations and variations. These concepts span from handling alterations in input data to managing external data shifts and adversarial attacks. The analysis revealed that deep learning models, especially those processing image data, are particularly susceptible to adversarial attacks, while high-dimensional tabular data like omics require robust feature extraction methods.
Understanding robustness requires a holistic approach that considers the entire lifecycle of a machine learning model. From data acquisition and preparation to model development and deployment, each stage introduces potential sources of variation. For instance, input perturbations often occur during data collection, impacting patient data integrity. In contrast, variations related to external data and domain shifts become apparent once the model is deployed in real-world settings. Model developers primarily observe variations during the specification and learning phases, whereas operators and healthcare professionals encounter issues post-deployment.
Robustness is not just a technical attribute but a fundamental principle for building trustworthy AI systems in healthcare. Ensuring models remain reliable and predictable in diverse clinical environments fosters confidence among stakeholders. By addressing the specific causes of perturbations throughout the model's lifecycle, we can enhance the overall reliability and safety of AI-driven healthcare solutions. This review underscores the need for a comprehensive framework to identify and mitigate potential sources of variation, ultimately improving the robustness of machine learning models in healthcare applications.
