Exploring Fairness and Performance Drivers Across State-of-the-Art Pulmonary Nodule Detection Algorithms

Researchers from Satsuma Lab have evaluated the fairness of deep learning based computer aided detection (CADe) systems for lung nodule detection in screening CT scans. Using data from a SUMMIT (London-based cohort), the study found that model performance remains consistent across sex and ethnic groups, despite imbalances in the training data. Results suggest that detection accuracy is driven more by nodule characteristics than by demographic factors. These findings support the equitable deployment of AI tools in future UK lung cancer screening programmes.

Further details can be found in John McCabe et al., 2025.