Adversarial Attack Prevention in Medical Imaging Deep Learning: A Comprehensive Review

Deep learning has also enhanced the predictability of medical imaging systems, allowing them to reliably diagnose using such modalities as X-ray, MRI, and CT scans. Nevertheless, all these models are extremely susceptible to adversarial attacks, where imperceptible modifications may result in critical misdiagnoses, casting serious doubts on the security and reliability of AI in healthcare. The paper is a review of the existing literature on preventive frameworks that can be used to secure medical imaging deep learning models against adversarial threats. It critically reviews the current defense mechanisms such as adversarial training, input preprocessing, robust architecture design, explainable AI integration, and hybrid security mechanisms. Comparative analysis shows the effectiveness of such methods in terms of performance (accuracy, precision, recall, and F1-score) and limitations of such methods in terms of robustness and interpretability. The results indicate that, despite the great strides made, the current methods do not have the generalized defense capabilities and cannot withstand the adaptive attacks. The paper highlights the necessity of comprehensive, extensible, and understandable security systems. These lessons are vital in creating credible AI systems that can be used in the real-world clinical decision support, medical diagnostics, and healthcare security infrastructures.