Adversarial Machine Learning in Healthcare

Adversarial Machine Learning in Healthcare: Defending AI Applications from Cyber Attack addresses an urgent and emerging security threat in healthcare: direct attacks against the artificial intelligence models used in diagnostics and treatment. Adversaries can learn how to fool these systems for example, by making insignificant changes to medical data (e.g., images, ECGs, etc.) that lead an AI to give the wrong diagnosis or by injecting malicious data used to train the AI.This book explains these specific threats in detail. More importantly, it focuses on the mitigations. It is a practical guide for building stronger, more secure AI systems that can resist these kinds of attacks. The book provides technical methods for developers to create robust AI models and frameworks for cybersecurity professionals to better defend their networks.Furthermore, these principles are extended to the broader clinical ecosystem. The book addresses the unique security challenges of interconnected IoT devices in smart hospitals and provide a practical guide to implementing Zero Trust Architectures for AI workflows. A significant portion of the book is also devoted to the complex ethical and regulatory dimensions, exploring accountability for compromised AI decisions and ensuring alignment with standards like GDPR and HIPAA.By integrating deep technical insights with practical implementation frameworks and essential ethical considerations, this book serves as an indispensable resource for the researchers, practitioners, and policymakers tasked with securing the next generation of clinical AI.