From Accuracy to Vulnerability: Quantifying the Impact of Adversarial Perturbations on Healthcare AI Models

Abstract

As AI becomes indispensable in healthcare, its vulnerability to adversarial attacks demands serious attention. Even minimal changes to the input data can mislead Deep Learning (DL) models, leading to critical errors in diagnosis and endangering patient safety. In this study, we developed an optimized Multi-layer Perceptron (MLP) model for breast cancer classification and exposed its cybersecurity vulnerabilities through a real-world-inspired adversarial attack. Unlike prior studies, we conducted a quantitative evaluation on the impact of a Fast Gradient Sign Method (FGSM) attack on an optimized DL model designed for breast cancer detection to demonstrate how minor perturbations reduced the model’s accuracy from 98% to 53%, and led to a substantial increase in the classification errors, as revealed by the confusion matrix. Our findings demonstrate how an adversarial attack can significantly compromise the performance of a healthcare AI model, underscoring the importance of aligning AI development with cybersecurity readiness. This research highlights the demand for designing resilient AI by integrating rigorous cybersecurity practices at every stage of the AI development lifecycle, i.e., before, during, and after the model engineering to prioritize the effectiveness, accuracy, and safety of AI in real-world healthcare environments.