Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals provides fundamental information on blockchain and digital twin technology as effective solutions in smart hospitals. Digital twin technology enables the creation of real-time virtual replicas of hospital assets and patients, enhancing predictive maintenance, operational efficiency, and patient care. Blockchain technology provides a secure and transparent platform for managing and sharing sensitive data, such as medical records and pharmaceutical supply chains. By combining these technologies, smart hospitals can ensure data security, interoperability, and streamlined operations while providing patient-centered care. The book also explores the impact of collected medical data from real-time systems in smart hospitals, and by making it accessible to all doctors via a smartphone or mobile device for fast decisions.
Inevitable challenges such as privacy concerns and integration costs must, of course, be addressed. However, the potential benefits in terms of improved healthcare quality, reduced costs, and global health initiatives makes the integration of these technologies a compelling avenue for the future of healthcare.
Some of the topics that readers will find in this book include:
Wireless Medical Sensor Networks in Smart Hospitals ? DNA Computing in Cryptography ? Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification ? Machine Learning-Enabled Digital Twins for Diagnostic And Therapeutic Purposes ? Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals ? Blockchain for Edge Association in Digital Twin Empowered 6G Networks ? Blockchain for Security and Privacy in Smart Healthcare ? Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Healthcare and Biomedical Sector ? Electronic Health Records in a Blockchain ? PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm ? AI and Transfer Learning Based Framework for Efficient Classification And Detection Of Lyme Disease ? Framework for Gender Detection Using Facial Countenances ? Smartphone-Based Sensors for Biomedical Applications ? Blockchain for Improving Security and Privacy in the Smart Sensor Network ? Sensors and Digital Twin Application in Healthcare Facilities Management ? Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy ? Machine Learning-Driven Digital Twins for Precise Brain Tumor and Breast Cancer Assessment ? Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare ? Digital Twin Application in Healthcare Facilities Management ? Cloud-based Digital Twinning for Structural Health Monitoring Using Deep Learning.
Audience
The book will be read by hospital and healthcare providers, administrators, policymakers, scientists and engineers in artificial intelligence, information technology, electronics engineering, and related disciplines.
Table of Contents
Preface xix
Part 1: Basic Fundamentals and Principles 1
1 Introduction to Smart Hospital 3
R. Bhuvana, R. J. Hemalatha, S. Baskar and Krishnakumar Kosalaram
1.1 Introduction 4
1.2 Conclusion 14
1.3 Demerits of Smart Hospitals 15
2 Wireless Medical Sensor Networks in Smart Hospitals 19
Renugadevi A. S., Jayaprakash M., Kaviya P., Kavin Raj P.N. and Jenish G.S.
2.1 Introduction 19
2.2 Wireless Sensor Network 21
2.3 Application in Healthcare 24
2.4 Benefits 33
2.5 Technical Challenges 34
2.6 Conclusion 36
3 Introduction of DNA Computing in Cryptography 39
M. Venkata Krishna Reddy, R. Ravinder Reddy, E. Padma Latha, Sirisha Alamanda and P.V.S. Srinivas
3.1 Introduction 39
3.2 Steganography 42
3.3 Related Work on DNA 43
3.4 DNA Computing 45
3.5 Essence of DNA Computing 46
3.6 Role of DNA Computing in Cryptography 47
3.7 Applications of DNA Computing 49
3.8 Related Work on DNA-Based Cryptography (Document) 51
3.9 Limitations 53
3.10 Cryptography Methods Based on DNA 54
3.11 Experimental Analysis 57
3.12 Conclusions and Future Work 58
Part 2: Methods and Applications 61
4 Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification 63
Asadi Srinivasulu
4.1 Introduction 64
4.2 Literature Review 64
4.3 Existing System 65
4.4 Proposed System 66
4.5 Experimental Results 69
4.6 Conclusion 74
5 Machine Learning-Enabled Digital Twins for Diagnostic and Therapeutic Purposes 77
Neel Shah, Jayansh Nagar, Kesha Desai, Nirav Bhatt, Nikita Bhatt and Hiren Mewada
5.1 Introduction 78
5.2 Conceptualization of Digital Twin and Machine Learning 79
5.3 State-of-the-Art Works 87
5.4 Applications of Digital Twins Enabled With Deep Learning Models and Reinforcement Learning 90
5.5 Limitations and Challenges 91
5.6 Opportunities/Future Scope 93
5.7 Concluding Remarks 93
6 Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals 99
C.M. Nalayini, V. Sathya, Shruthi Arunkumar and M. Dinesh Babu
6.1 Introduction 100
6.2 Smart Hospitals 101
6.3 Foundations of Blockchain Technology 104
6.4 Literature Survey 105
6.5 Integration of Blockchain in Healthcare 108
6.6 Digital Twin Technology in Smart Hospitals 110
6.7 Benefits and Challenges 111
6.8 Building A Connected Ecosystem 113
6.9 Regulatory Considerations 115
6.10 Case Study 117
6.11 Future Trends and Innovation 119
6.12 Conclusion 120
7 Blockchain for Edge Association in Digital Twin Empowered 6G Networks 123
C. Fancy, M. Anand and T. M. Sheeba
7.1 Introduction 123
7.2 Digital Twin Technology 125
7.3 Edge Computing in 6G Networks 127
7.4 The Blockchain Technology 129
7.5 Blockchain, Digital Twin, and Edge Computing Integration 133
7.6 Case Studies from Multiple Domains 139
7.7 Prospects for Future Directions and Research 144
8 Blockchain for Security and Privacy in the Smart Healthcare 153
V. Karthikeyan, S. Sridhar Raj, K. Gopalakrishnan, J. Dani Reagan Vivek and Anita Antwiwaa
8.1 Brief Overview of Medical Records and Their Confidentiality 153
8.2 Basics of Blockchain Technology 157
8.3 Benefits of BC Regarding the Protection of Medical Data 159
8.4 Principles of Using Blockchain for Medical Records 161
8.5 IAM on the Blockchain 162
8.6 Encrypted Medical Information Exchange via Blockchain 163
8.7 Insurance User Intelligence and Power in Blockchain-Enabled Services 165
8.8 Governmental and Moral Thoughts 166
8.9 Selected Experiences and Recommended Approaches 166
8.10 Prospects and Hurdles in Advancing Blockchain-Based Health Record Security 169
8.11 Conclusion and Future Prospects 173
9 Conceptual and Empirical Evidence for the Implementation of Blockchain Technology as a Solution for Healthcare Service Providers in India 179
B.C.M. Patnaik, Ipseeta Satpathy, Rocky Dwyer, Amit Kumar Tyagi and Anish Patnaik
10 Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based Healthcare and Biomedical Sectors 201
Amit Kumar Tyagi
10.1 Introduction 202
10.2 Various Applications of Blockchain and Internet of Things in Healthcare and Biomedical Sectors 203
10.3 Internet of Things Supported Blockchain Platforms in Healthcare and Biomedical Sectors 204
10.4 Blockchain Technology for Healthcare and Biomedical Sectors 205
10.5 Storage Capacity and Scalability for Electronic Health Records (EHR) 206
10.6 Security Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things 207
10.7 Privacy Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things 208
10.8 Trust Issue in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things 209
10.9 Other Issues Healthcare and Biomedical Sectors Rather than Security, Privacy, and Trust 210
10.10 Technical and Non-Technical Challenges in Healthcare and Biomedical Sectors 212
10.11 Future Work Toward Healthcare and Biomedical Sectors 213
10.12 Conclusion 214
11 Electronic Health Records in a Blockchain 219
Reshma V. and Rajesh Mamilla
12 A PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm 231
Ritu Aggarwal, Gulbir Singh and Eshaan Aggarwal
12.1 Introduction 231
12.2 Literature Review 232
12.3 Proposed Methodology 233
12.4 Machine Learning Algorithms 235
12.5 Experimental Setup 238
12.6 Conclusion 241
13 AI and Transfer Learning-Based Framework for Efficient Classification and Detection of Lyme Disease 245
Pramit Brata Chanda, Saikat Das, Sharanya Bhattacharya, Souhardya Biswas and Subir Kumar Sarkar
13.1 Introduction 246
13.2 Literature Survey 247
13.3 Methodologies 249
13.4 Proposed Work 254
13.5 Results and Analysis 260
13.6 Conclusion 264
14 Framework for Gender Detection Using Facial Countenances 269
Shyla, Shalu and Mohit Dayal
14.1 Introduction 269
14.2 Objectives 271
14.3 Methodology 273
14.4 Architecture 276
14.5 Raining and Evaluation 279
14.6 Conclusion 283
Part 3: Issues and Challenges 287
15 Unveiling the Challenges and Limitations in COVID-19 Health Data Prediction with Convolutional Neural Networks: A Data Science Research Perspective 289
Asadi Srinivasulu, Piyush Agrawal, Amit Agrawal and Goddindla Sreenivasulu
15.1 Introduction 289
15.2 Literature Review 291
15.3 Methodology 293
15.4 Challenges in COVID-19 Health Data Prediction 295
15.5 Limitations of Convolutional Neural Networks 298
15.6 Mitigation Strategies 299
15.7 Case Study: An Empirical Analysis of CNNs for COVID-19 Health Data Prediction 301
15.8 Conclusion 305
Part 4: Future Opportunities 309
16 Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning 311
K. Renugadevi, T. Jayasankar and J. ArputhaVijaya Selvi
16.1 Introduction to Cloud-Based Digital Twinning 312
16.2 Evolution of Structural Health Monitoring (SHM) 312
16.3 Digital Twinning: Concept and Applications 313
16.4 Integration of Cloud Computing in SHM 315
16.5 Deep Learning Techniques for Sensor Data Analysis 316
16.6 Leveraging Convolutional Neural Networks (CNNs) in Cloud Environment 318
16.7 Recurrent Neural Networks (RNNs) for Anomaly Detection 319
16.8 Proactive Maintenance and Early Fault Detection 319
16.9 Collaboration and Data Sharing in Cloud-Based Deployment 320
16.10 Anticipated Outcomes and Implications 321
16.11 Advancing SHM Technologies with Cloud-Based Solutions 322
16.12 Promoting Resilience and Sustainability through Intelligent SHM Systems 323
16.13 Conclusion 324
17 Smartphone-Based Sensors for Biomedical Applications 327
Amit Kumar Tyagi, Richa and Shabnam Kumari
17.1 Introduction to Smartphone-Based Sensors and Its Importance in Biomedical Applications 328
17.2 Smartphone-Based Sensors for Biomedical Applications 332
17.3 Benefits, Limitations, Issues, and Challenges of Smartphone-Based Sensors in Biomedical Application 333
17.4 Ensuring Data Security and Privacy in Biomedical Applications by Using Smartphone-Based Sensors 336
17.5 Sensor Technologies and Communication Protocols in Biomedical Applications 338
17.6 Data Processing and Analysis Using Emerging Technologies in Biomedical Applications 339
17.7 Future Research Directions in Biomedical Sensing Using Smartphone-Based Sensors 341
17.8 Conclusion 343
18 Blockchain for Improving Security and Privacy in the Smart Sensor Network 347
Amit Kumar Tyagi and Tanuj Surve
18.1 Introduction to Smart Sensor Networks and Blockchain Technology 348
18.2 Blockchain for Improving Security and Privacy in the Smart Sensor Network 356
18.3 Real-World Examples of Blockchain in Smart Sensor Networks 359
18.4 Issues and Challenges with Recommended Solutions of Using Blockchain in the Smart Sensor Network for Improving Security and Privacy in this Smart Era 360
18.5 Future Opportunities with Emerging Technologies in Blockchain for Smart Sensor Networks 361
18.6 Potential Advancements in Security and Privacy Using Emerging Technologies for Smart Sensor Networks 363
18.7 Incorporating Blockchain in Existing Sensor Networks for Better Efficiency 364
18.8 Conclusion 366
19 Sensors and Digital Twin Application in Healthcare Facilities Management 369
Amit Kumar Tyagi
19.1 Introduction to Healthcare Facilities Management 369
19.2 Digital Twins: Concepts and Applications 373
19.3 Facilities Management with Digital Twins for Effective Healthcare Facilities 376
19.4 Benefits and Disadvantages of Emerging Technologies in Modern Healthcare Facilities 377
19.5 Security and Privacy Issues in Modern Healthcare Facilities 379
19.6 Data Security in Healthcare Facilities 381
19.7 Real-World Examples of Sensor and Digital Twin Implementation/Solution for Better Healthcare Facilities Management 383
19.8 Challenges and Recommended Solutions for Better Healthcare Facilities Management 385
19.9 Future Trends and Innovations Toward Better Healthcare Facilities Management 386
19.9.1 The Future of Digital Twins in Healthcare 387
19.10 Conclusion 388
20 Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy 391
Silky Pareyani, Neeta Nathani and Jagdeesh Kumar Ahirwar
20.1 Introduction 392
20.2 Motivation 394
20.3 Background 394
20.4 State of the Art 402
20.5 Technical Challenges 403
20.6 Significant Future Trends of Blockchain in Healthcare 406
20.7 Conclusion 407
21 Advancing Healthcare Diagnostics: Machine Learning-Driven Digital Twins for Precise Brain Tumor and Breast Cancer Assessment 413
J. Olalekan Awujoola, T. Aniemeka Enem, F. N. Ogwueleka, O. Abioye and E. Abidemi Awujoola
21.1 Introduction 414
21.2 Digital Twin 416
21.3 The Contribution of Machine Learning and Deep Learning to the Advancement of Digital Twins in Healthcare 418
21.4 Machine Learning in Cancer and Brain Prediction 419
21.5 Materials and Methods 423
21.6 Experimental Results and Analysis 425
21.7 Conclusion and Recommendation 430
22 Digital Twin Applications in Healthcare Facilities Management 435
Kandan M., Naveen P., G. Nagarajan and S. Janagiraman
22.1 Introduction to Digital Twin Technology in Healthcare 436
22.2 Adoption of Digital Twins in Healthcare Facility Management 436
22.3 Evolution from Engineering and Manufacturing to Healthcare 437
22.4 Real-Time Virtual Duplicates for Facility Management 438
22.5 Features of Digital Twins: Sensors, Data Analytics, and Simulation 440
22.6 Challenges in Healthcare Facility Management 440
22.7 Resource Allocation and Patient Safety 442
22.8 Operational Efficiency in Healthcare Facilities 443
22.9 Monitoring Infrastructure, Equipment, and Patient Movement 444
22.10 Integration of Sensor Data EHRs, and Other Sources 445
22.11 Empowering Stakeholders with Insights from Digital Twins 445
22.12 Continuous Improvement and Adaptive Management in Healthcare 446
22.13 Conclusion 447
23 Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare 451
Md Mahfujur Rahman
23.1 Introduction 451
23.2 Balancing Ethics and Faith: AI and Blockchain in Halal Healthcare 453
23.3 AI-Driven Halal Healthcare: Navigating Compliance and Technological Integration 456
23.4 Streamlining Halal Healthcare via Blockchain 459
23.5 AI and Blockchain in Halal Healthcare: Regulatory Frontiers 462
23.6 Conclusion 463
Acknowledgment 464
References 464
Index 467
