Full Description
The convergence of emerging technologies is reshaping industries at an unprecedented pace, and healthcare is no exception. With the rapid digitization of medical records, the proliferation of connected health devices, and the growing need for secure and efficient data management, the healthcare sector faces both immense opportunities and daunting challenges. Among the most promising solutions to these challenges are Blockchain Technology and Federated Learning—two revolutionary paradigms that offer a decentralized, privacy-preserving approach to healthcare innovation.
Decentralized Healing: Transforming Healthcare with Federated Learning and Blockchain Technologies, aims to serve as a comprehensive guide to understanding and implementing these transformative technologies in the healthcare ecosystem. The chapters in this book cover a diverse range of topics, from fundamental concepts and theoretical frameworks to real-world applications and case studies. Readers will gain insights into how federated learning enables collaborative AI training while preserving patient data privacy, how blockchain ensures data integrity, security, and traceability, and how the integration of these technologies can drive innovations in electronic health records, cybersecurity, and decentralized healthcare networks. A major highlight of this book is its focus on practical applications and future trends. Whether you are a healthcare professional, Blockchain/AI researcher, policymaker, or industry leader, this book offers valuable knowledge and actionable insights into how decentralized approaches can empower patients, improve medical decision-making, and revolutionize global healthcare infrastructures.
This book serves as a valuable resource for anyone passionate about the future of healthcare and technology. By embracing decentralized and privacy-preserving solutions, we can pave the way for a more secure, efficient, and patient-centric healthcare system.
Let the journey towards Decentralized Healing begin!
Contents
Chapter 1 Recent trends for individual Virtual Healthcare Chapter 2 Securing the Future of Health: A Survey on Data Protection and Privacy Techniques in a Decentralized System Chapter 3 Integrating Blockchain and Federated Learning for Secure and Decentralized Data Management Chapter 4 Fundamentals of Blockchain and Federated Learning: Concepts, Types, and ApplicationsFundamentals of Blockchain and Federated Learning: Concepts, Types, and Applications Chapter 5 Enhancing AI Privacy and Security: The Synergy of Blockchain and Federated Learning Chapter 6 Federated Learning: Enabling Decentralized AI While Preserving Data Privacy Chapter 7 Improving Electronic Health Record Security with Blockchain: A Comprehensive Analysis of Current Innovations and Future Directions Chapter 8 Secure Federated Learning Framework for Training Deep Learning Models Chapter 9 Blockchain for Electronic Health Records: Advancements, Applications, and Future Prospects Chapter 10 Blockchain in Healthcare: Enhancing Security, Interoperability, and Efficiency Chapter 11 Revolutionizing Electronic Health Records: Blockchain for Secure and Efficient Healthcare Data Management Chapter 12 Revolutionizing Healthcare Systems with Federated Learning and Blockchain: Insights from Case Studies Chapter 13 Securing and Managing Data in Federated Learning for Healthcare Cyber-Physical Systems: A Blockchain Approach. Chapter 14 Blockchain Security in Healthcare: Ensuring Data Integrity and Privacy Chapter 15 Enhancing Security and Efficiency in Healthcare Through Decentralized and Blockchain-Based Solutions Chapter 16 Protection of data and privacy in decentralized healthcare Chapter 17 Collaborative Healthcare Networks for Medicine Chapter 18 LedgerMed Recognising the Perfect Ledger for Patient-Centric Healthcare through Blockchain Chapter 19 The Future of Healthcare Finance with Blockchain: Predictions and Trends Chapter 20 The Interoperability Challenge in Healthcare Chapter 21 Way Ahead: Decentralized Healing Based on Blockchain and Federated Learning
