ArticleScientific reports2026
Explainable AI for chest radiographs: sex-stratified fairness auditing in CNN-based pneumonia detection.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Artificial intelligence (AI) systems for chest X-ray (CXR) interpretation can achieve high standalone diagnostic accuracy, yet limited transparency raises concerns about subgroup disparities. We audited sex-stratified performance and attention patterns in CNN-based pneumonia detection using the RSNA Pneumonia Detection Challenge dataset (26,684 frontal adult CXRs). ImageNet-pretrained InceptionV3 models were trained across five prespecified random seeds and combined as a seed-ensemble. Analyses used a prespecified, fixed, patient-level sex- and label-balanced test set (n = 1,000; 250 pneumonia-positive and 250 pneumonia-negative per sex) and a single validation-derived operating point held constant across sex. Baseline performance was robust (AUROC 0.859 [0.836, 0.881], Brier 0.155 [0.141, 0.169]). Males showed higher discrimination than females (AUROC 0.882 vs 0.837, gap + 0.045 [+ 0.002, + 0.091]) and a lower false-positive rate (FPR gap - 0.073 [- 0.145, - 0.006]). To contextualize disparities, we quantified Grad-CAM attention relative to lung segmentation masks; in-lung activation was lower in females (60.8% vs 64.7%, M - F + 3.9 pp [+ 1.6, + 6.4]). A combined mitigation (group-balanced sampling + adversarial debiasing) preserved performance (AUROC 0.864 [0.843, 0.885]) while reducing AUROC/specificity gaps and attenuating the false-positive disparity. Quantitative explainability can complement fairness auditing and mitigation in medical imaging.
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