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.

Rejwan Bin Sulaiman is currently serving as a Lecturer in Cyber Security at the University of Law, United Kingdom. He earned his Ph.D. in Artificial Intelligence and Cybersecurity from the University of Bedfordshire, where his doctoral research focused on federated learning-based approaches for secure and privacy-preserving financial AI systems. He has held academic positions at several institutions, including Northumbria University and Arden University.
Dr. Sulaiman’s research spans Cybersecurity, Artificial Intelligence, Computer Vision, and Machine Learning, with particular interest in developing decentralized AI models that enhance data security and user privacy. His work has been published in leading venues such as IEEE, Springer, and CRC Press, contributing to the advancement of secure machine learning frameworks in distributed environments.
He is a Certified Ethical Hacker (CEH) and the founder of STEMResearch.Ai, an initiative that supports and mentors early-career researchers in STEM fields. He is also a Fellow of the Higher Education Academy (FHEA) and has received multiple awards recognizing his innovative teaching practices and dedication to academic excellence.

Vitaly Schetinin is a Senior Lecturer in Machine Learning within the School of Computer Science and Technology at the University of Bedfordshire, UK. His research expertise spans machine learning, statistical modelling, decision-making under uncertainty, Bayesian learning, Markov chain Monte Carlo (MCMC) simulations, and signal and image processing. Previously, he conducted research in Bayesian machine learning at the University of Exeter and held senior research roles at Jena University Hospital in Germany.
Dr Schetinin has led externally funded research, including a Leverhulme Trust project on Bayesian discovering of patterns in EEG (£82,500) and an EPSRC-backed industrial study on Probabilistic modelling of indoor wave propagation (£85,200). His applied research focuses particularly on safety-critical and clinical applications, combining advanced machine learning with practical decision-support systems. He also co-developed TraumaCalc, a predictive system designed to support clinical risk assessment and trauma care.

Abdur Rahman Salih, an Assistant Professor in AI and Cyber Security, a highly dedicated, professional, academic researcher with extensive knowledge with industrial and Academic experience for 23 years in the commercial and higher education sector. Proficient in developing and teaching Programming Languages; (C++, Python and Java), Network Security, Computer Forensics and Artificial Intelligence Algorithms. He has published numerous papers in well-known Conferences and Journals and written book chapters in CS & AI. Currently, he is a director of three PG programmes: MSc AI Technology, MSc Cyber Security Technology and MSc Tech for Sustainable Cities at Northumbria University in London and a Cyber Security Consultant / SW developer (freelance) at Naztech Inc. NY, USA

Maruf Farhan is currently serving as a Lecturer in Cybersecurity and Computer Science at Ulster University (London campus, QAHE), Coventry University London (Dagenham), and the University of Sussex International Study Centre. With over six years of industry and two years of academic experience, he is actively involved in teaching and developing curricula on topics such as cybersecurity, ethical hacking, blockchain, and AI integration across undergraduate computing modules.
He holds an MSc in Cybersecurity (Distinction) from Northumbria University, where his dissertation explored the use of blockchain to detect counterfeit products. He is also currently working toward achieving the Associate Fellowship of the Higher Education Academy (AFHEA), demonstrating his commitment to research-informed and inclusive teaching practices.
Maruf has contributed to several international research conferences, including ICISS and IEEE iCACCESS, and has published book chapters and peer-reviewed articles on blockchain, healthcare IoT security, and federated learning. Notably, he has authored two book chapters in Taylor & Francis' Blockchain and AI in Healthcare.
His expertise includes federated learning, penetration testing, and securing AI systems in healthcare. He brings unique insight into the intersection of privacy-preserving AI and cybersecurity, positioning him as an ideal co-editor and contributor for this volume. Maruf is a professional member of IEEE and ACM and is passionate about mentoring students and early-career researchers in ethical, impactful tech research.