Subgroup Performance of a Commercial Digital Breast Tomosynthesis Model for Breast Cancer Detection

While research has established the potential of AI models for mammography to improve breast cancer screening outcomes, there have not been any detailed subgroup evaluations performed to assess the strengths and weaknesses of commercial models for digital breast tomosynthesis (DBT) imaging. This study presents a granular evaluation of the Lunit INSIGHT DBT model on a large retrospective cohort of 163,449 screening mammography exams from the Emory Breast Imaging Dataset (EMBED). Model performance was evaluated in a binary context with various negative exam types (162,081 exams) compared against screen detected cancers (1,368 exams) as the positive class. The analysis was stratified across demographic, imaging, and pathologic subgroups to identify potential disparities. The model achieved an overall AUC of 0.91 (95% CI: 0.90-0.92) with a precision of 0.08 (95% CI: 0.08-0.08), and a recall of 0.73 (95% CI: 0.71-0.76). Performance was found to be robust across demographics, but cases with non-invasive cancers (AUC: 0.85, 95% CI: 0.83-0.87), calcifications (AUC: 0.80, 95% CI: 0.78-0.82), and dense breast tissue (AUC: 0.90, 95% CI: 0.88-0.91) were associated with significantly lower performance compared to other groups. These results highlight the need for detailed evaluation of model characteristics and vigilance in considering adoption of new tools for clinical deployment.

Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains

Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal performance, we argue that decreases in fairness can be either harmful or non-harmful, depending on the type of change and how sensitive attributes are used. To this end, we introduce the notion of positive-sum fairness, which states that an increase in performance that results in a larger group disparity is acceptable as long as it does not come at the cost of individual subgroup performance. This allows sensitive attributes correlated with the disease to be used to increase performance without compromising on fairness. We illustrate this idea by comparing four CNN models that make different use of the race attribute in the training phase. The results show that removing all demographic encodings from the images helps close the gap in performance between the different subgroups, whereas leveraging the race attribute as a model's input increases the overall performance while widening the disparities between subgroups. These larger gaps are then put in perspective of the collective benefit through our notion of positive-sum fairness to distinguish harmful from non harmful disparities.