Full Description
Securing Health: The Convergence of AI and Cybersecurity in Healthcare explores how emerging technologies are revolutionizing modern medicine, ensuring not just innovation but safety and trust in the digital age. As artificial intelligence and cybersecurity rapidly transform industries, this timely book offers an in-depth look at their powerful convergence within the healthcare sector.
Organized into four comprehensive parts, the book unveils cutting-edge strategies and practical applications shaping the future of medicine. From blockchain integration and smart wearables to adversarial machine learning and federated learning, each chapter offers critical insights into securing sensitive data, enhancing diagnostic accuracy, and preserving patient privacy.
Discover how blockchain is being used to protect medical records and power real-time health monitoring for athletes, with a special focus on deployments in regions like Kurdistan. Learn how AI-driven tools are predicting complex diseases like cerebral palsy and post-transplant diabetes, offering hope for earlier intervention and better outcomes. Dive into the world of deep learning as it redefines medical imaging, from detecting COVID-19 via X-rays to advancing brain MRI segmentation.
With contributions grounded in global case studies and research, Securing Health addresses the ethical, technical, and regulatory challenges of modern healthcare technology. Whether you're a medical professional, tech innovator, or policy maker, this book equips you with the knowledge to navigate and lead in an era where AI and cybersecurity aren't just enhancements—they're essentials.
Empowering, forward-thinking, and solution-focused, Securing Health is your essential guide to the future of secure, intelligent healthcare.
Contents
Part I. Blockchain in Healthcare, 1. A Framework for the Integration of Blockchain Technology in Healthcare Records Management Systems in Kurdistan, 2. A Blockchain-Based Approach to Healthcare Data Security: Leveraging Anomaly Detection and Smart Contracts, 3. Integrating AI, Quantum Computing, and Blockchain for Secure Healthcare Data Management Addressing Regulatory and Implementation Challenges, 4. Smart Wearables: Revolutionizing Premier League Footballers with IoT and Blockchain Technology, 5. Blockchain Technology for Healthcare Data Security and Privacy, Part II. Security in Healthcare, 6. Adversarial Machine Learning in Healthcare: Protecting AI Systems from Threats to Patient Safety and Data Security, 7. Federated Learning in Healthcare: Distributed Machine Learning for Privacy Preservation, 8. GAN and Explainable AI (XAI) for Cybersecurity in Healthcare, 9. Impact of GDPR on Healthcare Data Security Practices, Part III. Artificial Intelligence in Disease Prediction and Analysis, 10. Leveraging AI in the Diagnosis and Management of Cerebral Palsy in Children: A Case Study of Chattagram Maa-O-Shishu Hospital, 11. Predicting New-Onset Diabetes Post-Transplantation in Kidney Patients through Assessment of Medication Dosages: A Boosting Ensemble Machine Learning Method, 12. Predicting Quality of Care Outcomes in Diabetes Patients Using Boosted Tree Models: A Clinical Factor Analysis, Part IV. Artificial Intelligence in Medical Imaging, 13. Deep Learning-Based Medical Image Processing, 14. Deep Learning in Computerized Medical Imaging: Diagnosing Lung Diseases (Pneumonia and COVID-19) from X-Rays, 15. Exploring the Frontiers of Brain MRI Image Segmentation Using Deep Learning
