Intelligent healthcare systems refer to the use of technologies, such as IoT, big data, AI, and machine learning, to improve the quality and efficiency of healthcare delivery. Healthcare data is both valuable and sensitive, and patients' data is increasingly at risk from cyber attacks on medical devices and critical healthcare infrastructure, posing serious risks to patient health and safety or compromising sensitive user data. As healthcare provision relies upon numerous interconnected systems, to facilitate the diagnosis or treatment of patients' health conditions, these systems rely on advanced cryptography to secure and protect sensitive user information.

• Chapter 1: Clinically Constrained Cryptography: Device-Class Mappings, Protocol Profiles, and Deployment Playbooks for Intelligent Healthcare Systems
• Chapter 2: Context Integrity for FHIR-Based Healthcare Systems Using Authenticated Encryption
• Chapter 3: Privacy-Preserving Federated Learning and Audit-Grade Integrity for Intelligent Healthcare Matching Systems
• Chapter 4: A Reproducible zkSNARK Benchmark Framework for Privacy-First Healthcare Workflows: From Circuits to Operational Metrics
• Chapter 5: Enhancing Security Beyond Cryptography in Healthcare AI Systems through Input-Output Sanitization
• Chapter 6: Hidden Vulnerabilities, Ethical Concerns, and Governance Issues in Advanced Cryptography for Intelligent Healthcare Systems
• Chapter 7: Ethical-by-Design Cryptography: A Governance Framework for Intelligent Healthcare Systems
• Chapter 8: Promotion of Data Autonomy to Support Insights From Patient Health Data Records in Africa
• Chapter 9: Novel Case Studies on Advanced Cryptography for Intelligent Healthcare Systems
• Chapter 10: The Future of Cryptographic Algorithm Development for Intelligent Healthcare Systems