Privacy-Preserving AI in Healthcare

Privacy-Preserving AI in Healthcare: Techniques and Applications provide a comprehensive exploration of how artificial intelligence can revolutionize healthcare while safeguarding sensitive patient information. The book examines the growing role of AI in clinical decision-making, medical imaging, predictive analytics, drug discovery, and remote patient monitoring, alongside the critical need for privacy, security, and ethical compliance. It introduces key privacy-preserving techniques such as data anonymization, differential privacy, federated learning, homomorphic encryption, and secure multi-party computation, explaining their practical applications in modern healthcare systems. Readers will gain insights into regulatory frameworks including HIPAA and GDPR, challenges in Electronic Health Records (EHR) management, and strategies for secure healthcare data sharing. The book also addresses ethical concerns such as fairness, transparency, and bias in AI-driven healthcare. Designed for researchers, students, healthcare professionals, and technology practitioners, this book highlights future directions and innovations in secure and trustworthy healthcare AI.