Detection and Validation of Hidden Subgroup Disparities in Clinical AI

Machine learning (ML) models used in clinical workflows often perform systematically differently across patient groups, raising concerns about equity and patient safety. Traditional subgroup analyses can expose such disparities only when rich metadata (e.g., patient demographics) is available and captures the sources of performance variation, conditions rarely met in practice. Algorithmic subgroup discovery methods offer a solution by identifying coherent groups from learned feature representations, but because true underlying stratifications are unknown, these methods are hard to evaluate. As a result, subgroup discovery has not been systematically applied or assessed for performance monitoring. Our main objective is to evaluate algorithmic subgroup discovery for performance monitoring, assessing the discovered subgroups' performance gaps and semantic coherence. Investigating cardiomegaly classification in a dataset of 223,228 chest X-rays, we showed that the discovered subgroups exhibited larger performance gaps (accuracies of 0.61-0.90) than traditional metadata-based analysis (0.80-0.94) and were frequently interpretable using patient, acquisition, and model metadata. As a proof of concept, we also showed that annotations from learned subgroups can be used to mitigate the biases they expose. We conclude that subgroup discovery is a practical and effective tool to enhance the performance validation and monitoring of ML systems in healthcare.