Diagnostic Reliability with Adversarial Robustness in Chest Radiograph Models

Abstract

Deep learning models have demonstrated remarkable proficiency in medical image analysis, particularly in the automated interpretation of chest radiographs. However, the well-documented susceptibility of these highly parameterized models to adversarial perturbations raises critical concerns regarding their overall diagnostic reliability in real-world clinical settings. This study investigates the complex and often obscured relationship between adversarial robustness and diagnostic reliability through the lens of a rigorous causal modeling framework. By imposing a structural causal model on the classification pipeline of chest radiographs, we systematically isolate the causal effects of adversarial noise on diagnostic outcomes, effectively differentiating spurious statistical correlations from genuine pathological feature representations. Our methodology involves the generation of sophisticated adversarial examples using iterative gradient-based attacks on widely utilized convolutional neural networks trained on public chest radiograph datasets. The implemented causal framework enables the precise quantification of how robustness interventions, such as adversarial training and causal feature alignment, affect diagnostic accuracy across diverse patient demographic subgroups and varying imaging acquisition environments. The empirical findings reveal a direct, quantifiable causal link where strategic enhancements in adversarial robustness systematically improve diagnostic reliability, significantly mitigating the risks of misdiagnosis caused by subtle image artifacts or distribution shifts. This research underscores the absolute necessity of integrating causal inference methodologies into model evaluation and training, ensuring that deep learning tools deployed in high-stakes healthcare environments are not only accurate in controlled experimental domains but also reliably robust against deliberate and inadvertent perturbations encountered in daily clinical practice.