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Full Description
This book introduces a novel integration of Federated Learning with the vision of Healthcare 5.0 to enable secure, adaptive, and intelligent health systems. It presents cutting-edge frameworks that support decentralized model training across medical institutions while preserving patient privacy and ensuring compliance with data regulations.
Focusing on real-world use cases, such as predictive diagnostics, edge-based patient monitoring, personalized medicine, and surgical robotics, it bridges theoretical advances with practical implementations. This book provides deep insights into the design of scalable, privacy-preserving artificial intelligence infrastructures suited for cross-institutional collaboration.
It is designed for artificial intelligence researchers, digital health architects, healthcare technologists, and policy advisors. This supports the development of human-centric, resilient, and interoperable smart healthcare ecosystems.
Contents
Understanding Healthcare 5.0 and Emerging Technologies.- Fundamentals of Federated Learning: Principles and Applications.- Data Privacy Challenges in Artificial Intelligence-Driven Healthcare.- Regulatory Frameworks: HIPAA, GDPR, and Compliance in Federated Learning.- Real Time Patient Monitoring and IOMT Applications.- Integration of Blockchain Technology for Ensuring Trust and Security in the Digital Health Market: A Comprehensive Review.- The Convergence of Federated Learning for the Digital Healthcare Market: An Overview.- Differential Privacy and Homomorphic Encryption in Healthcare Artificial Intelligence.- Analysis of Consumer Emotions Impacted By COVID-19.- Guiding The Development of AI In Healthcare Through Ethical Considerations and Effective Governance.- Intelligent Workforce Management in Healthcare 5.0: Redefining HR Through Federated Learning.- The Legal Labyrinth of Smart Wearable Medical Devices: A Literary Overview.- From Traditional to Intelligent: Transforming Global Health Care through Innovation.- Ethical Considerations of Emotion AI used in the Synthetic Media Generations and Applications.- Machine Learning-Based Prediction of Gene-Disease Associations for Reliable Evidence.- Addressing Computational Overhead in Federated Learning Models in Healthcare 5.0 and Beyond.- Robustness Against Adversarial Attacks and Model Security in Healthcare 5.0 and Beyond.-Scalable Model Aggregation and Interoperability Solutions in Healthcare Systems.- Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in Healthcare 5.0.- Federated Learning Architectures: Centralized Vs. Decentralized Models In Human Resource(HR).- A Two-staged Optimized Stacking Ensemble learning Classifier for the Prediction of Cervical Cancer.- AI-Assisted Histopathological Image Analysis for Automated Gastric Cancer Detection.- Robotics and AI-Powered Surgical Interventions in Gastric Cancer: Enhancing Precision and Efficacy of Oncologic Treatment24. Electronic Health Records using Blockchain.- Centralized vs. Decentralized Federated Learning Architectures: Design Trade-offs, Security, and Performance in Healthcare 5.0 Applications.- Navigating Healthcare 5.0: How Emerging Technologies Are Transforming Care Delivery and Medical Innovation.- Identification of Stress in IT Professionals Using Convolutional Neural Network.- Federated Learning for Precision Medicine: A Blockchain Enhanced Framework for Privacy Preserving Predictive Analytics in Healthcare 5.0.- Machine Learning Advancements for Diabetes Prediction with LightGBM.- Blockchain Integration for Enhanced Trust and Security in Federated Learning for Healthcare 5.0.- Ontology-Based Data Harmonization and Federated Transfer Learning: Enabling Scalable and Interoperable Intelligence in Healthcare 5.0 for Next-Generation Healthcare.- Future Trends in Federated Learning for Next-Generation Healthcare.- Advancing Federated Learning in Healthcare 5.0.- A Futuristic Pathway in Healthcare.- Federated Learning in Healthcare Finance: A Systematic Review of Privacy-Preserving Models.- AI-Induced Digital Addiction: Its Impact on Human Relationships within Healthcare 5.0 Ecosystems.- Real-Time Detection of Latent Infections Using LSTM and IoMT-Based Health Monitoring.- Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems.- Neuro-Symbolic Federated Learning Models for Diagnostic Intelligence in Healthcare 5.0.- Reducing Computational Overhead in Federated Learning: A Comprehensive Analysis.- Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions.
