AI-Driven Smart Healthcare : Powered by Hyperscale Computing and Next Generation Networks

Pratap, Ajay/ Singh Patel, Yashwant/ Narayan Yadav, Ram

Wiley-IEEE Press（2025/12発売）

• 製本 Hardcover:ハードカバー版／ページ数 352 p.
• 言語 ENG
• 商品コード 9781394297030
• DDC分類 610.28563

Full Description

Reimagine the future of healthcare with a deep dive into hyperscale computing and distributed networks

In AI-Driven Smart Healthcare: Powered by Hyperscale Computing and Next Generation Networks, a team of distinguished researchers delivers an insightful and practical discussion of the healthcare applications of artificial intelligence and fog-enabled next-generation networks. The book provides practical insights and methodologies for the design, development, and deployment of these technologies throughout the healthcare industry.

Readers will explore key areas of recent advancement, including the Internet of Things, fog computing, artificial intelligence, machine learning, serverless computing, and blockchain in a way that allows them to assess the feasibility and scalability of a variety of technological healthcare solutions.

The book also includes:

A thorough introduction to the integration of AI and fog computing into smart healthcare systems
Comprehensive explorations of how these technologies enhance healthcare delivery, with examples like remote patient monitoring and advanced diagnostic models
Practical discussions of the advantages, challenges, and potential solutions associated with AI and fog computing
An interdisciplinary focus for professionals working at the intersection of AI, machine learning, fog computing, and healthcare

Perfect for researchers, practitioners, and other healthcare stakeholders, AI-Driven Smart Healthcare will also benefit technologists, educators, hospital administrators, and other professionals with an interest in the application of the latest technologies to recurrent and significant issues in the field of healthcare.

Contents

Notes on Contributors xv
Preface xvii
Acknowledgments xix
List of Abbreviations xxi
Introduction xxv

1 Internet of Things, Edge, Fog, and Data Analytics in Smart Healthcare: Introduction, Benefits, and Challenges 1
1.1 Introduction 1
1.2 Use of Edge and Fog Computing for Healthcare Applications 3
1.2.1 Role of Edge Computing in Resource Management 7
1.3 Data Analytics in Healthcare Applications 8
1.3.1 Components of BDA 9
1.4 BDA Applications 10
1.5 Use Case Scenarios 13
1.6 Current Challenges and Future Directions 17
1.7 Future Direction: Opening Up Health Data for Research 19
1.8 Conclusion 20

2 Hyperscale Computing Paradigm in Healthcare 29
2.1 Introduction 29
2.2 The Evolution of Computing in Healthcare 30
2.3 What Is Hyperscale Cloud? 32
2.4 Components of Hyperscale Computing 32
2.5 Challenges of Hyperscale Computing in Healthcare 37
2.6 Hyperscale Data Centers 39
2.7 Tech Giants Playing a Role in Hyperscaling 40
2.8 Public Versus Private Hyperscale Clouds in Healthcare 42
2.9 Depth and Future of Hyperscale Computing in Healthcare 42
2.10 Case Studies of Hyperscale Computing in Healthcare 45

3 Containerized Internet of Medical Things and Serverless Computing for Smart Healthcare Systems 49
3.1 Wireless Technologies Empowering Internet of Medical Things 49
3.2 IoMT and Its Role in Modern Healthcare 50
3.3 Importance of Containerization 52
3.4 Serverless Computing: Concept and Overview 54
3.5 Complementing Containerization for Healthcare Applications 55
3.6 Real-world Use Cases 57
3.7 Future Directions in Containerized IoMT and Serverless Healthcare 58
3.8 Conclusion 58

4 Kubernetes Enabled Resource Management Architecture for Secure Innovation in Healthcare 61
4.1 Overview of Kubernetes and Its Role in Healthcare 61
4.2 Key Features of Kubernetes 62
4.3 Key Kubernetes Concepts 63
4.4 Kubernetes Control Plane and Nodes 64
4.5 Kubernetes Resource Management 65
4.6 Benefits of Kubernetes for the Healthcare Industry 66
4.7 Use Cases of Cloud-native and Kubernetes in Healthcare 67
4.8 Kubernetes as a Solution 68
4.9 Conclusion 70

5 Exploring Artificial Intelligence (AI) and Machine Learning (ML) for Performance and Predictive Analysis of Various Diseases Using Health-related Data 73
5.1 Introduction 73
5.2 Challenges in Healthcare 75
5.3 Significance of AI and ML in Healthcare 76
5.4 Application of AI/ML in Healthcare System 78
5.5 Major Development Phases of AI/ML-based Healthcare Systems 81
5.6 Secure, Private, and Robust AI/ML-based Healthcare: Challenges 85
5.7 Use Case: Diabetes 87
5.8 Challenges and Future Directions 93
5.9 Conclusion 93

6 Algorithmic Frameworks for Cost Minimization in Criticality Aware Mobile Healthcare System 101
6.1 Introduction 101
6.2 Related Works 102
6.3 System Model 104
6.4 Problem Definition 108
6.5 Proposed Auction Mechanism 111
6.6 Analysis of Proposed Mechanism 117
6.7 Performance Study 120
6.8 Conclusion 124

7 Utility-aware Edge Computing System for Remote Health Monitoring 127
7.1 Introduction 127
7.2 Related Works 129
7.3 System Model and Problem Formulation 131
7.4 Proposed Auction Model 136
7.5 Analysis of Proposed Auction Model 142
7.6 Performance Evaluation 143
7.7 Conclusion 148

8 Fog Computing-based WBAN and IoT Framework for Prediction of Various Diseases Using Big Data Analytics 151
8.1 Introduction 151
8.2 The Role of Fog Computing in Healthcare 153
8.3 Big Data Analytics in Healthcare 153
8.4 Wireless Body Area Networks in Healthcare 155
8.5 Framework Design 156
8.6 Disease Prediction Use Case for Healthcare 158
8.7 Conclusion 163

9 Disease Spread Detection and Controlling with Fog-based Model in Wireless Body Area Networks 167
9.1 Compartmental Model 169
9.2 Simulation Tools 174
9.3 Other Environmental Factors to Control Spread of Disease 177
9.4 AI- and ML-based Health Prediction Approaches 178
9.5 Conclusion 180

10 Optimized Doctor Recommendation System Using Machine Learning Approach 185
10.1 Introduction 185
10.2 Related Works 187
10.3 System Model 190
10.4 Proposed Approach 195
10.5 Performance Study 201
10.6 Conclusion and Future Work 204

11 UAV-enabled Smart Healthcare Application for Next-generation Wireless Networks 209
11.1 Introduction 209
11.2 Related Works 211
11.3 System Model 213
11.4 Federated Deep Reinforcement Learning 223
11.5 A Direction for Performance Study 230
11.6 Conclusion 232

12 A Road Map for Personalized Medicine: Challenges and Innovations 239
12.1 Introduction 239
12.2 Use of Genome Data to Develop Personalized Medicine 243
12.3 Use of Medical Imaging Data to Develop Personalized Medicine 245
12.4 Use of AI and ML in Drug Development in Personalized Medicine 247
12.5 Use of Digital Twin for Personalized Medicine 251
12.6 Current Challenges and Future Directions 253

13 Delay-sensitive, Privacy-preserving Blockchain-enabled Fog-assisted Framework for Smart Healthcare 263
13.1 Introduction 263
13.2 Related Work 265
13.3 System Model 267
13.4 Problem Formulation 271
13.5 Proposed Solution 278
13.6 Experimental Results 286
13.7 Conclusion 294

A Research Discussion, Tools, and Use Cases 299
A.1 Artificial Intelligence in Smart Healthcare 299
A.2 Research Trends in AI for Healthcare 300
A.3 Use Cases of AI in Smart Healthcare 300
A.4 Key Tools and Frameworks for AI-driven Healthcare 309
A.5 Challenges and Limitations in AI-driven Healthcare 312
A.6 Conclusion 313

References 314
Index 319