Adversarial Attacks on AI Diagnostic Tools: Assessing Risks and Developing Mitigation Strategies

Abstract

Abstract:
Artificial Intelligence (AI) diagnostic tools are increasingly utilized in healthcare for disease detection, prognosis, and personalized treatment planning. However, their growing reliance on machine learning algorithms makes them vulnerable to adversarial attacks—subtle, often imperceptible manipulations to input data that can lead to incorrect or misleading outcomes. These attacks pose significant threats to patient safety, clinical decision-making, and the integrity of healthcare systems. This paper critically examines the nature and risks of adversarial attacks on AI-based diagnostic systems, including examples in radiology, dermatology, and pathology, where altered inputs have led to misclassifications. The study categorizes different attack vectors such as white-box, black-box, and physical-world attacks, assessing their feasibility and potential impact on real-world healthcare applications. Additionally, the paper explores current defense mechanisms including adversarial training, input preprocessing, and model verification techniques, highlighting their strengths and limitations. A risk assessment framework is proposed to systematically evaluate the vulnerability of AI models based on model architecture, data sensitivity, and operational context. The paper also emphasizes the importance of regulatory oversight, continuous model auditing, and stakeholder education in minimizing risk. Through an interdisciplinary approach combining technical, ethical, and policy dimensions, the study aims to inform the development of more resilient AI diagnostic tools. Ultimately, enhancing the robustness of these systems is essential not only for ensuring accurate and trustworthy diagnostics but also for preserving public confidence in AI-driven healthcare innovations.