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Artificial Intelligence-Enabled Blockchain Technology and Digital Twin for Smart Hospitals

Edited by Amit Kumar Tyagi
Copyright: 2024   |   Status: Published
ISBN: 9781394287390  |  Hardcover  |  
490 pages
Price: $225 USD
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One Line Description
The book uniquely explores the fundamentals of blockchain and digital twin and their uses in smart hospitals.

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.

Description
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

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Author / Editor Details
Amit Kumar Tyagi, PhD, is an assistant professor at the National Institute of Fashion Technology, New Delhi, India. He obtained his doctorate in 2018. He has published more than 200 papers in refereed international journals, conferences, and books, many of which are with the Wiley-Scrivener imprint. He has filed more than 25 national and international patents in deep learning, the Internet of Things, cyberphysical systems, and computer vision. His current research focuses on next-generation machine-based communications, blockchain technology, smart and secure computing, and privacy.

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Table of Contents
Preface
Part 1: Basic Fundamentals and Principles
1. Introduction to Smart Hospital
R. Bhuvana, R. J. Hemalatha, S. Baskar and Krishnakumar Kosalaram
1.1 Introduction
1.1.1 Aspects That Make Up Intelligent Hospitals
1.1.2 Advantages That Smart Hospitals Offer
1.1.3 Hierarchical Structure of Smart Hospital
1.1.4 Smart Hospital Management System
1.2 Conclusion
1.3 Demerits of Smart Hospitals
References
2. Wireless Medical Sensor Networks in Smart Hospitals
Renugadevi A. S., Jayaprakash M., Kaviya P., Kavin Raj P.N. and Jenish G.S.
2.1 Introduction
2.2 Wireless Sensor Network
2.3 Application in Healthcare
2.3.1 Patient Monitoring
2.3.1.1 Heart Rate Monitoring
2.3.1.2 Blood Pressure Monitoring
2.3.1.3 Body Temperature Monitoring
2.3.1.4 Respiratory Rate Monitoring
2.3.2 Telemedicine
2.3.2.1 Environmental Monitoring
2.3.2.2 Temperature and Humidity Control
2.3.2.3 Rehabilitation Monitoring
2.3.2.4 Emergency Response System (ERS)
2.3.2.5 Clinical Trials and Response
2.4 Benefits
2.5 Technical Challenges
2.6 Conclusion
References
3. Introduction of DNA Computing in Cryptography
M. Venkata Krishna Reddy, R. Ravinder Reddy, E. Padma Latha, Sirisha Alamanda and P.V.S. Srinivas
3.1 Introduction
3.1.1 Cryptography Key Management
3.2 Steganography
3.3 Related Work on DNA
3.4 DNA Computing
3.5 Essence of DNA Computing
3.6 Role of DNA Computing in Cryptography
3.7 Applications of DNA Computing
3.7.1 Steganography Using DNA
3.7.2 Chip Technology Using DNA
3.8 Related Work on DNA-Based Cryptography (Document)
3.9 Limitations
3.10 Cryptography Methods Based on DNA
3.11 Experimental Analysis
3.12 Conclusions and Future Work
References
Part 2: Methods and Applications
4. Enhancing Diabetic Retinopathy and Glaucoma Diagnosis through Efficient Retinal Vessel Segmentation and Disease Classification
Asadi Srinivasulu
4.1 Introduction
4.2 Literature Review
4.3 Existing System
4.3.1 Drawbacks
4.3.1.1 Complexity and Expertise Dependency
4.3.1.2 Technological Hurdles
4.3.1.3 Performance and Verification
4.3.2 Input Data
4.4 Proposed System
4.4.1 Streamlined Expertise Requirements
4.4.2 Scalable Technology Implementation
4.4.3 Robust Performance and Validation
4.4.4 Advantages
4.4.4.1 Enhanced Accessibility
4.4.4.2 Improved Scalability
4.4.4.3 Consistently Reliable Performance
4.4.5 Proposed Algorithm Steps
4.5 Experimental Results
4.5.1 Performance Evaluation Methods
4.5.1.1 Accuracy
4.5.1.2 Precision
4.5.1.3 Recall
4.5.1.4 Sensitivity
4.5.1.5 Specificity
4.5.1.6 F1 Score
4.5.1.7 Area Under Curve (AUC)
4.5.1.8 Convolutional Neural Network (CNN) Architecture
4.5.1.9 Model Training and Validation
4.5.1.10 Data Augmentation and Regularization
4.5.1.11 Performance Metrics
4.6 Conclusion
Conflicts of Interest
References
5. Machine Learning–Enabled Digital Twins for Diagnostic and Therapeutic Purposes
Neel Shah, Jayansh Nagar, Kesha Desai, Nirav Bhatt, Nikita Bhatt and Hiren Mewada
5.1 Introduction
5.2 Conceptualization of Digital Twin and Machine Learning
5.2.1 Digital Twins
5.2.1.1 Working With the Digital Twins
5.2.2 Machine Learning
5.2.2.1 Deep Learning
5.2.2.2 Reinforcement Learning
5.3 State-of-the-Art Works
5.4 Applications of Digital Twins Enabled With Deep Learning Models and Reinforcement Learning
5.5 Limitations and Challenges
5.6 Opportunities/Future Scope
5.7 Concluding Remarks
References
6. Blockchain as the Backbone of a Connected Ecosystem of Smart Hospitals
C.M. Nalayini, V. Sathya, Shruthi Arunkumar and M. Dinesh Babu
6.1 Introduction
6.2 Smart Hospitals
6.2.1 Key Technologies in Smart Hospital Environments
6.2.1.1 Internet of Things (IoT)
6.2.1.2 Artificial Intelligence (AI)
6.2.1.3 Big Data Analytics
6.2.1.4 Interoperable Systems
6.2.2 Challenges and Opportunities in Implementing Smart Hospital Solutions
6.3 Foundations of Blockchain Technology
6.4 Literature Survey
6.5 Integration of Blockchain in Healthcare
6.5.1 Use Cases of Blockchain in Healthcare
6.5.2 Improving Data Interoperability and Integrity
6.5.3 Enhancing Security and Privacy in Healthcare Transactions
6.6 Digital Twin Technology in Smart Hospitals
6.6.1 Applications of Digital Twins in Healthcare
6.6.2 Synergies Between Blockchain and Digital Twin Technologies
6.7 Benefits and Challenges
6.7.1 Benefits
6.7.2 Challenges and Considerations
6.8 Building A Connected Ecosystem
6.8.1 Role of Blockchain in Creating A Connected Healthcare Ecosystem
6.8.2 Interoperability and Data Exchange in Smart Hospitals
6.8.3 Collaborative Approaches for Ecosystem Development
6.9 Regulatory Considerations
6.9.1 Compliance and Legal Aspects in Implementing Blockchain in Healthcare
6.9.2 Future Regulatory Trends and Implications
6.10 Case Study
6.11 Future Trends and Innovation
6.12 Conclusion
References
7. Blockchain for Edge Association in Digital Twin Empowered 6G Networks
C. Fancy, M. Anand and T. M. Sheeba
7.1 Introduction
7.1.1 Background and Motivation
7.1.2 Scope
7.2 Digital Twin Technology
7.2.1 Fundamentals
7.2.2 Utilization in 6G Networks
7.2.3 Obstacles and Opportunities
7.3 Edge Computing in 6G Networks
7.3.1 Edge Computing – An Overview
7.3.2 Edge Computing’s Significance in 6G Networks
7.3.3 Opportunities
7.4 The Blockchain Technology
7.4.1 Essential Elements of Blockchain
7.4.2 Blockchain Use Cases in Telecommunications
7.4.3 Edge Association in 6G Networks – An Initiative
7.5 Blockchain, Digital Twin, and Edge Computing Integration
7.5.1 Theoretical Framework
7.5.2 Schematic Requirements
7.5.3 Construction Challenges and Solutions
7.6 Case Studies from Multiple Domains
7.6.1 Intelligent Urban Infrastructure and Smart Cities
7.6.2 Industrial IoT and Production
7.6.3 Telemedicine and Healthcare
7.7 Prospects for Future Directions and Research
7.7.1 Evolving Trends in 6G Networks
7.7.2 Research Gaps and Opportunities for Improvement
References
8. Blockchain for Security and Privacy in the Smart Healthcare
V. Karthikeyan, S. Sridhar Raj, K. Gopalakrishnan, J. Dani Reagan Vivek and Anita Antwiwaa
8.1 Brief Overview of Medical Records and Their Confidentiality
8.1.1 Medical Records
8.1.2 Patient-Centered Network Design
8.1.3 Significance of Clinical-Care Data
8.1.4 Discretion
8.1.5 Overview Regarding the Safety of Medical Records
8.2 Basics of Blockchain Technology
8.3 Benefits of BC Regarding the Protection of Medical Data
8.4 Principles of Using Blockchain for Medical Records
8.5 IAM on the Blockchain
8.6 Encrypted Medical Information Exchange via Blockchain
8.6.1 Protected Information and Better Safety
8.6.2 Cooperation and the Efficient Transfer of Data
8.7 Insurance User Intelligence and Power in Blockchain-Enabled Services
8.8 Governmental and Moral Thoughts
8.9 Selected Experiences and Recommended Approaches
8.9.1 Exploring Real-World Situations: A Global View
8.9.2 Exemplary Methods
8.10 Prospects and Hurdles in Advancing Blockchain-Based Health Record Security
8.10.1 Forthcoming Steps
8.10.2 Difficulties in Implementing BC
8.11 Conclusion and Future Prospects
References
9. Conceptual and Empirical Evidence for the Implementation of Blockchain Technology as a Solution for Healthcare Service Providers in India
B.C.M. Patnaik, Ipseeta Satpathy, Rocky Dwyer, Amit Kumar Tyagi and Anish Patnaik
Introduction
Review of Literature
Research Objectives
Scope of the Study
Research Design
Sample Size Determination for Unknown Populations
Tools Applied
Analysis of data
Challenges
The Future of Healthcare with Blockchain
Conclusion
Scope for Future Study
Acknowledgments
References
Annexures
10. Blockchain-Enabled Internet of Things (IoTs) Platforms for IoT-Based
Healthcare and Biomedical Sectors
Amit Kumar Tyagi
10.1 Introduction
10.2 Various Applications of Blockchain and Internet of Things in Healthcare and Biomedical Sectors
10.3 Internet of Things Supported Blockchain Platforms in Healthcare and Biomedical Sectors
10.4 Blockchain Technology for Healthcare and Biomedical Sectors
10.5 Storage Capacity and Scalability for Electronic Health Records (EHR)
10.6 Security Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things
10.7 Privacy Issues in Healthcare and Biomedical Sectors: Weaknesses and Threats in Blockchain-Based Internet of Things
10.8 Trust Issue in Healthcare and Biomedical Sectors: Weaknesses and Threats
in Blockchain-Based Internet of Things
10.9 Other Issues Healthcare and Biomedical Sectors Rather than Security, Privacy, and Trust
10.10 Technical and Non-Technical Challenges in Healthcare and Biomedical Sectors
10.11 Future Work Toward Healthcare and Biomedical Sectors
10.12 Conclusion
References
11. Electronic Health Records in a Blockchain
Reshma V. and Rajesh Mamilla
Introduction
Blockchain in Healthcare
Structure of EHR
Components of Electronic Health Records
Effectiveness of Electronic Health Records
Categories and Life Span of EHR
EHR Adoption in India
Challenges of Blockchain
Conclusion
References
12. A PSO-Based Hybrid Cardiovascular Disease Prediction for Using Artificial Flora Algorithm
Ritu Aggarwal, Gulbir Singh and Eshaan Aggarwal
12.1 Introduction
12.1.1 Research Gap and Findings
12.2 Literature Review
12.3 Proposed Methodology
12.3.1 Proposed Flow Work
12.3.2 Algorithm for Artificial Flora Algorithm
12.4 Machine Learning Algorithms
12.4.1 Naïve Bayes
12.4.2 Support Vector Machine
12.4.3 KNN
12.4.4 Convolutional Neural Network (CNN)
12.4.5 Particle Swarm Optimization
12.4.6 Performance Evaluation
12.4.7 Confusion Matrix
12.5 Experimental Setup
12.6 Conclusion
References
13. AI and Transfer Learning–Based Framework for Efficient Classification and Detection of Lyme Disease
Pramit Brata Chanda, Saikat Das, Sharanya Bhattacharya, Souhardya Biswas and Subir Kumar Sarkar
13.1 Introduction
13.2 Literature Survey
13.3 Methodologies
13.3.1 Deep Learning and Image Processing
13.3.1.1 Convolutional Neural Network
13.3.1.2 Activation Functions
13.3.2 Transfer Learning
13.3.2.1 Transfer Learning for Image Classification
13.3.2.2 Models Used for Transfer Learning
13.4 Proposed Work
13.4.1 Data Collection
13.4.2 Data Preprocessing
13.4.3 Sequential Model Method
13.4.3.1 Model Creation
13.4.3.2 Model Training and Fitting
13.4.4 Transfer Learning Method
13.4.4.1 Fully Connected Layer Creation for Different Models Selected
13.5 Results and Analysis
13.5.1 Deep Learning and Transfer Learning Models Evaluation
13.6 Conclusion
References
14. Framework for Gender Detection Using Facial Countenances
Shyla, Shalu and Mohit Dayal
14.1 Introduction
14.1.1 Gender Detection Process
14.1.2 Significance of Gender Recognition in AI
14.1.3 Practical Implications of Gender Recognition
14.1.4 Technological Advancements in Gender Recognition
14.1.5 Overview and Future Considerations
14.2 Objectives
14.2.1 Previous Approaches and Limitations SVM on Images and FERET Database
14.2.2 Challenges and Limitations
14.2.3 Threefold Objectives of the Exploration Investigating Technological Advancements
14.2.4 Comprehending Psychological and Sociological Factors
14.2.5 Examining Ethical Implications
14.2.6 Exploration Framework
14.2.7 Technological Advancements in AI
14.2.8 Psychological and Sociological Factors
14.2.9 Ethical Considerations
14.2.10 Methodological Approach
14.2.11 Face Detection
14.2.12 Image Processing
14.2.13 Deformable Spatial Pyramid (DSP)
14.2.14 Evaluation Metrics
14.2.15 Accuracy
14.2.16 Binary Cross-Entropy Loss
14.3 Methodology
14.3.1 Training a Gender Detection Model
14.3.1.1 Data Preparation
14.3.1.2 Data Augmentation
14.3.1.3 Model Architecture
14.3.1.4 Model Compilation and Training
14.3.2 Real-time Gender Detection Using Webcam
14.3.2.1 Loading the Pre-Trained Model
14.3.2.2 Webcam Initialization
14.3.2.3 Face Detection and Gender Classification Loop
14.3.2.4 Display Output and User Interaction
14.3.2.5 Resource Cleanup
14.3.3 Integration of Deep Learning
14.3.4 Model Training and Pre-Trained Models
14.3.5 Real-Time Face Processing
14.4 Architecture
14.4.1 Key Features
14.4.2 Components
14.4.3 Applications
14.4.4 Benefits
14.5 Raining and Evaluation
14.5.1 Training the Model Data Preparation
14.5.2 Evaluation of the Model Performance Metrics
14.5.3 Future Directions and Considerations
14.5.4 Outcome
14.6 Conclusion
References
Part 3: Issues and Challenges
15. Unveiling the Challenges and Limitations in COVID-19 Health Data Prediction with Convolutional Neural Networks: A Data Science Research Perspective
Asadi Srinivasulu, Piyush Agrawal, Amit Agrawal and Goddindla Sreenivasulu
15.1 Introduction
15.1.1 Background
15.1.2 Objectives of the Study
15.1.3 Scope and Limitations
15.2 Literature Review
15.2.1 COVID-19 Data Prediction Techniques
15.2.2 Convolutional Neural Networks in Health Data Analysis
15.2.3 Previous Studies on CNNs for COVID-19 Health Data Prediction
15.3 Methodology
15.3.1 Data Collection and Preprocessing
15.3.2 CNN Model Architecture
15.3.3 Evaluation Metrics
15.4 Challenges in COVID-19 Health Data Prediction
15.4.1 Insufficient and Biased Data
15.4.2 Labeling and Annotation Errors
15.4.3 Data Imbalance
15.4.4 Feature Selection and Extraction
15.4.5 Limitations of Existing System
15.5 Limitations of Convolutional Neural Networks
15.5.1 Complexity and Computational Demands
15.5.2 Interpretability and Explainability
15.5.3 Generalization to New COVID-19 Variants
15.5.4 Ethical and Privacy Concerns
15.5.5 Insufficient and Biased Data
15.6 Mitigation Strategies
15.6.1 Data Augmentation and Balancing Techniques
15.6.2 Transfer Learning and Pre-Trained Models
15.6.3 Ensemble Methods and Model Averaging
15.6.4 Interpretability Techniques
15.7 Case Study: An Empirical Analysis of CNNs for COVID-19 Health Data Prediction
15.7.1 Dataset Description
15.7.2 Experimental Setup
15.7.3 Results and Discussion
15.7.4 Performance Evaluation Methods
15.7.4.1 Accuracy
15.7.4.2 Precision
15.7.4.3 Recall
15.7.4.4 Sensitivity
15.7.4.5 Specificity
15.7.4.6 F1 Score
15.7.4.7 Area Under the Curve (AUC)
15.8 Conclusion
15.8.1 Summary of Findings
15.8.2 Implications for Future Research
15.8.3 Recommendations for Model Developers and Practitioners
Data Availability
Funding Statement
Conflict of Interest
Author Contributions
References
Part 4: Future Opportunities
16. Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning
K. Renugadevi, T. Jayasankar and J. ArputhaVijaya Selvi
16.1 Introduction to Cloud-Based Digital Twinning
16.2 Evolution of Structural Health Monitoring (SHM)
16.3 Digital Twinning: Concept and Applications
16.4 Integration of Cloud Computing in SHM
16.5 Deep Learning Techniques for Sensor Data Analysis
16.6 Leveraging Convolutional Neural Networks (CNNs) in Cloud Environment
16.7 Recurrent Neural Networks (RNNs) for Anomaly Detection
16.8 Proactive Maintenance and Early Fault Detection
16.9 Collaboration and Data Sharing in Cloud-Based Deployment
16.10 Anticipated Outcomes and Implications
16.11 Advancing SHM Technologies with Cloud-Based Solutions
16.12 Promoting Resilience and Sustainability through Intelligent SHM Systems
16.13 Conclusion
References
17. Smartphone-Based Sensors for Biomedical Applications
Amit Kumar Tyagi, Richa and Shabnam Kumari
17.1 Introduction to Smartphone-Based Sensors and Its Importance in Biomedical Applications
17.1.1 Principles of Sensing Technologies
17.1.2 Types of Sensors Compatible with Smartphones
17.1.3 Organization of the Work
17.2 Smartphone-Based Sensors for Biomedical Applications
17.3 Benefits, Limitations, Issues, and Challenges of Smartphone-Based Sensors
in Biomedical Application
17.4 Ensuring Data Security and Privacy in Biomedical Applications by Using Smartphone-Based Sensors
17.5 Sensor Technologies and Communication Protocols in Biomedical Applications
17.6 Data Processing and Analysis Using Emerging Technologies in Biomedical Applications
17.7 Future Research Directions in Biomedical Sensing Using Smartphone-Based Sensors
17.8 Conclusion
References
18. Blockchain for Improving Security and Privacy in the Smart Sensor Network
Amit Kumar Tyagi and Tanuj Surve
18.1 Introduction to Smart Sensor Networks and Blockchain Technology
18.1.1 Basics of Sensor Technology
18.1.2 Applications and Importance of Smart Sensor Networks
18.1.3 Security and Privacy Issues in Smart Sensor Networks
18.1.4 The Importance of Security and Privacy in Today’s Era
18.1.5 Blockchain: Definitions, Key Components, Types, and Applications in This Smart Era
18.1.6 Importance of Blockchain for Security and Privacy in Smart Sensor Network
18.1.7 Organization of the Work
18.2 Blockchain for Improving Security and Privacy in the Smart Sensor Network
18.2.1 Privacy-Preserving Techniques in the Smart Sensor Network
18.2.2 Privacy-Enhancing Applications in Smart Sensor Networks
18.3 Real-World Examples of Blockchain in Smart Sensor Networks
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
18.5 Future Opportunities with Emerging Technologies in Blockchain for Smart Sensor Networks
18.6 Potential Advancements in Security and Privacy Using Emerging Technologies for Smart Sensor Networks
18.7 Incorporating Blockchain in Existing Sensor Networks for Better Efficiency
18.8 Conclusion
References
19. Sensors and Digital Twin Application in Healthcare Facilities Management
Amit Kumar Tyagi
19.1 Introduction to Healthcare Facilities Management
19.1.1 Available Sensors in Healthcare Facilities
19.1.2 Evolution of Healthcare Facilities
19.2 Digital Twins: Concepts and Applications
19.2.1 The Role of Sensors and Digital Twins
19.2.2 Digital Twins’ Use Cases and Advantages in Healthcare Facilities
19.3 Facilities Management with Digital Twins for Effective Healthcare Facilities
19.4 Benefits and Disadvantages of Emerging Technologies in Modern Healthcare Facilities
19.5 Security and Privacy Issues in Modern Healthcare Facilities
19.6 Data Security in Healthcare Facilities
19.7 Real-World Examples of Sensor and Digital Twin Implementation/Solution
for Better Healthcare Facilities Management
19.8 Challenges and Recommended Solutions for Better Healthcare Facilities Management
19.9 Future Trends and Innovations Toward Better Healthcare Facilities Management
19.9.1 The Future of Digital Twins in Healthcare
19.10 Conclusion
References
20. Integration of Internet of Medical Things (IoMT) with Blockchain Technology to Improve Security and Privacy
Silky Pareyani, Neeta Nathani and Jagdeesh Kumar Ahirwar
20.1 Introduction
20.2 Motivation
20.3 Background
20.3.1 Blockchain Components
20.3.2 Electronic Health Record Security & Privacy (without Blockchain)
20.3.3 Issues in the Storage of Electronic Medical Records
20.3.4 Blockchain-Powered Information Exchange and Storage
20.3.5 Healthcare Domains
20.4 State of the Art
20.5 Technical Challenges
20.6 Significant Future Trends of Blockchain in Healthcare
20.7 Conclusion
References
21. Advancing Healthcare Diagnostics: Machine Learning–Driven Digital Twins
for Precise Brain Tumor and Breast Cancer Assessment
J. Olalekan Awujoola, T. Aniemeka Enem, F. N. Ogwueleka, O. Abioye and E. Abidemi Awujoola
21.1 Introduction
21.2 Digital Twin
21.2.1 Evolution of Digital Twin: A Historical Perspective
21.3 The Contribution of Machine Learning and Deep Learning to the Advancement of Digital Twins in Healthcare
21.4 Machine Learning in Cancer and Brain Prediction
21.5 Materials and Methods
21.5.1 Dataset
21.5.2 Methodology
21.6 Experimental Results and Analysis
21.7 Conclusion and Recommendation
References
22. Digital Twin Applications in Healthcare Facilities Management
Kandan M., Naveen P., G. Nagarajan and S. Janagiraman
22.1 Introduction to Digital Twin Technology in Healthcare
22.2 Adoption of Digital Twins in Healthcare Facility Management
22.3 Evolution from Engineering and Manufacturing to Healthcare
22.4 Real-Time Virtual Duplicates for Facility Management
22.5 Features of Digital Twins: Sensors, Data Analytics, and Simulation
22.6 Challenges in Healthcare Facility Management
22.7 Resource Allocation and Patient Safety
22.8 Operational Efficiency in Healthcare Facilities
22.9 Monitoring Infrastructure, Equipment, and Patient Movement
22.10 Integration of Sensor Data EHRs, and Other Sources
22.11 Empowering Stakeholders with Insights from Digital Twins
22.12 Continuous Improvement and Adaptive Management in Healthcare
22.13 Conclusion
References
23. Ethical and Technological Convergence: AI and Blockchain in Halal Healthcare
Md Mahfujur Rahman
23.1 Introduction
23.2 Balancing Ethics and Faith: AI and Blockchain in Halal Healthcare
23.2.1 Ethical and Devotional Directives
23.2.2 Halal Compliance and Preserving Health
23.2.3 Ensuring Fairness and Transparency in Halal Healthcare
23.2.4 Empowering Patient Consent
23.2.5 Harmonizing Technological Innovation
23.3 AI-Driven Halal Healthcare: Navigating Compliance and Technological Integration
23.3.1 Patient Dietary Management During Fasting
23.3.2 AI in Halal Pharma: Ensuring Compliance and Integrity
23.3.3 AI in Enhancing Patient Choice and Privacy
23.3.4 AI-Driven Enhanced Healthcare Certification
23.4 Streamlining Halal Healthcare via Blockchain
23.4.1 Blockchain Revolution in Halal Supply Chain
23.4.1.1 Transforming the Halal Healthcare Supply Chain
23.4.1.2 Combating Counterfeiting and Decentralizing
23.4.2 Blockchain Revolution in Halal Certification
23.4.2.1 Advancing Halal Authenticity and Transparency
23.4.2.2 Streamlining Digital Certification and Smart Contracts and Digital
23.4.3 Navigating Halal Healthcare Data Management
23.4.3.1 Addressing Data Complexity with Decentralization
23.4.3.2 Empowering Patients and Enhancing Clinical Trial Reliability
23.4.3.3 Linking Outcomes to Costs for Fairness
23.4.3.4 Securing and Verifying Medical Data
23.5 AI and Blockchain in Halal Healthcare: Regulatory Frontiers
23.5.1 Ethical and Legal Challenges in AI-Driven Halal Healthcare
23.5.2 Governance and Data Security in Halal Healthcare AI
23.5.3 Blockchain’s Role in Enhancing Transparency and Security
23.6 Conclusion
Acknowledgment
References
Index

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