Securing AI of Healthcare: A Selective Review on Identifying and Preventing Adversarial Attacks

The growing use of AI in healthcare enables doctors to swiftly uncover illness patterns that they might have overlooked initially, especially in medical images such as X-rays. However, health records are meant only for patients and their doctors. Yet, with advancing technology, attackers can manipulate sensitive data, risking undetected life-threatening diseases due to adversarial attacks on AI models. An attacker can manipulate cutting-edge medical image classification models using Fast Gradient Sign Method (FGSM) attack. For instance, attackers might introduce subtle changes to medical images, e.g., skin cancer moles, to misguide a deep convolutional neural network (CNN) to produce incorrect decisions. Although this attack is undetectable to the human eye, it is still strategically designed to mislead the CNN model into misclassifying the images. As a result, the model misdiagnoses the underlying disease leading to erroneous treatment recommendations or decisions. Our purpose for this study is to identify adversarial attacks’ impact on healthcare systems and then to identify prevention techniques for these attacks, especially in healthcare systems where solutions are scarce and attacks are increasing. We identify adversarial attack threat vectors in healthcare systems and mitigation techniques against these adversarial attacks. This study will guide us in ensuring safe and secure healthcare systems.