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Submit a ProposalThe Impact of Algorithmic Technologies on Healthcare
 | Edited by Parul Dubey, Mangala Madankar, Pushkar Dubey and Bui Thanh Hung Series: Machine Learning in Biomedical Science and Healthcare Informatics Copyright: 2025 | Status: Published ISBN: 9781394305469 | Hardcover | 468 pages Price: $225 USD  |
One Line Description
The book explores the fundamental principles and transformative
advancements in cutting-edge algorithmic technologies, detailing their
application and impact on revolutionizing healthcare.
Audience
This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.
This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.
Description
This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine.
After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field.
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This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine.
After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field.
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Author / Editor Details
Parul Dubey, PhD, is an assistant professor in the Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur, India. Her academic and research focus spans various areas of computer science and IT. She has published approximately 50 works, including journal articles, conference papers, and book chapters, along with 17 Indian patents.
Parul Dubey, PhD, is an assistant professor in the Department of Artificial Intelligence, G. H. Raisoni College of Engineering, Nagpur, India. Her academic and research focus spans various areas of computer science and IT. She has published approximately 50 works, including journal articles, conference papers, and book chapters, along with 17 Indian patents.
Mangala Madankar, PhD, is the head of the Department of Artificial Intelligence at G. H. Raisoni College of Engineering, Nagpur, India. Her research interests include natural language processing, data science, big data, and information retrieval systems. She has published over 55 research papers in international journals and conferences.
Pushkar Dubey, PhD, is an assistant professor and head of the Department of Management at Pandit Sundarlal Sharma (Open) University, Chhattisgarh, Bilaspur. He has published more than 70 research papers in reputed journals, secured 7 patents, and completed 5 research projects.
Bui Thanh Hung, PhD, is affiliated with the Data Science Laboratory, Data Science Department, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam. His research focuses on natural language processing, machine learning, machine translation, and text processing. He has published two books and approximately 40 conference papers.
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Table of Contents
Preface
1. Introduction to Algorithmic Health: Exploring Healthcare Through Digital Twins
A.S. Vinay Raj, N. Gopinath, R. Anandh, M. Mohammed Jalaluddin and Lyndsay R. Buckingham
1.1 Introduction
1.2 Related Works
1.2.1 Reviews on Wearable Sensors
1.3 Hardware Description
1.3.1 Sensor Board
1.3.2 Power Requirements
1.3.3 ECG Sensor Measurements
1.3.4 PPG Sensor
1.3.5 Power Consumption
1.4 Methodology
1.4.1 Healthcare Monitoring System
1.4.2 Heart Rate Monitoring with ECG
1.4.3 Heart Rate Analysis
1.4.4 BP Analysis
1.4.5 Human Body Temperature Analysis
1.4.6 IoT-Based Remote Monitoring System
1.4.7 Network Connectivity
1.4.8 Network Gateway
1.4.9 Server
1.4.10 Database
1.4.11 Security
1.5 Performance Analysis
1.6 Conclusion
References
2. The Digital Revolution in Healthcare
Devanand Bhonsle, Rama Shukla, Deepshikha Sahu, Tanuja Kashyap, Monika Dewangan and Seema Mishra
2.1 Introduction
2.1.1 Challenges
2.1.2 Motivation
2.2 Digital Technologies in the Healthcare Sector
2.2.1 Electronic Health Record
2.2.2 Remote Patient Monitoring
2.2.3 Artificial Intelligence
2.2.4 Telemedicine
2.2.5 Federated Learning
2.3 Evolution of Digitalization in Business
2.3.1 Industry 4.0 and Business Intelligence
2.3.2 New Opportunities to Traditional Management
2.3.3 Role of Digitalization in Healthcare
2.3.4 Industry 4.0 in Healthcare Sector
2.4 Role of IoMT in Healthcare
2.4.1 Internet of Medical Things Architecture
2.5 Internet of Medical Things Devices
2.5.1 Wearable Devices
2.5.2 Smart Medical Devices
2.5.3 Telemedicine Devices
2.5.4 Smart Home Devices
2.6 Security and Privacy in the Healthcare Sector
2.7 Eliminating Security and Privacy Concerns of Digitalization of the Healthcare Sector
2.7.1 Threats to the Healthcare Sector
2.7.2 Developing Medical Equipment Management Programs
2.7.3 How are Medical Data Stored in Software Like EHR?
2.8 Discussion
2.9 Future Works
2.9.1 Research on Privacy and Security in the Healthcare Sector for the Better
2.9.2 Understanding the Impact of Digitization
2.9.3 Investigation of Privacy and Security Risks Associated with Wearable Technology
2.9.4 Analysis of Role of AI in Healthcare
2.9.5 Understand the Role of Block Chain Technology in Healthcare
2.9.6 Study on the Role of Patient Engagement in Privacy and Security
2.10 Conclusion
References
3. Data-Driven Diagnostics: Deep Learning for Brain Tumor Classification
Astha Pathak and Lalita Panika
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.4 Result Analysis
3.5 Conclusion
References
4. Predictive Analysis in Patient Care
Bolukonda Prashanth, Bandi Krishna, Rakesh Nayak, Umashankar Ghugar and Arunakranthi Godishala
4.1 Introduction
4.2 Review of Predictive Analysis
4.3 Conclusion and Future
References
5. Leveraging Predictive Analytics: Enhancing Brain Tumor Classification with XGBoost
Katakam Hemanvitha and Vikram Dhiman
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.4 Results and Discussion
5.5 Conclusion
References
6. Machine Learning in Medical Imaging Revolutionizing Lung Cancer Diagnosis: A Comparative Analysis of Convolutional Neural Networks and Vision Transformers in Medical Imaging
Priya Parkhi, Bhagyashree Hambarde, Himesh Gangwani, Rupali Vairagade and Fred Kalombo
6.1 Introduction
6.2 Literature Review
6.3 Description of Model
6.4 Methodology
6.5 Results
6.6 Conclusion
References
7. Innovations in AI and ML for Medical Imaging: An Extensive Study with an Emphasis on Face Spoofing Detection and Snooping
Aparna Pandey, Arvind Kumar Tiwari, Harsha Nishad and Siji A. Thomas
7.1 Introduction
7.2 Artificial Intelligence as Well as Device Understandings
7.3 Assaults Through Entrance Spoofing
7.4 A Case Study with Real-Time Narrative: Identifying Face Spoofing in Medical Imaging
7.5 Moral Factors to Consider
7.6 Discussion
7.7 Summary
References
8. Progressive Growing of Generative Adversarial Networks (PGGAN) Approach to Synthesize Medical Images
Vishal V. Raner, Amit D. Joshi, Suraj T. Sawant and Tamizharasan P. S.
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 Results and Discussion
8.5 Conclusions
Acknowledgments
References
9. Revolutionizing Healthcare Through Optimized Video Retrieval
Pratibha Singh and Alok Kumar Singh Kushwaha
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.3.1 Dataset
9.4 Results and Discussion
9.5 Conclusion
References
10. Multiclass Classification of Oral Diseases Using Deep Learning Models
Mohammed Zubair Hussain, Shrey Gupta, Bhagyashree Hambarde, Priya Parkhi and Zafar Karimov
10.1 Introduction
10.2 Literature Review
10.3 Methodology
10.4 Results
10.5 Conclusion
References
11. Smart Wearable Devices for Remote Patient Monitoring in Healthcare
Ravi Mishra, Swati Chaitandas Hadke, Devanand Bhonsle, Priti Nilesh Bhagat, Anupama Mahabansi and Sheetal Mungale
11.1 Introduction
11.2 Wearable Devices for Remote Monitoring
11.3 Communication Technologies for Remote Healthcare Monitoring
11.4 Proposed Methodology
11.5 Conclusion
References
12. Efficient IoT Solutions for Remote Health Monitoring
Vijayakumar S., N. Sheik Hameed, Kanchan S. Tiwari, A. Allwyn Sundarraj, N. Gopinath and Lyndsay R. Buckingham
12.1 Introduction
12.1.1 Introduction to DT
12.1.2 Digital Twin in the Healthcare Sector
12.2 Related Works
12.3 Methodology
12.3.1 IoT in Healthcare
12.3.2 IoT in Digital Healthcare
12.3.3 Internet of Medical Things (IoMT) Devices
12.3.4 Security and Privacy Solutions
12.4 Discussion
12.4.1 Risks to the Healthcare Industry
12.4.2 Creating Management Programs for Medical Equipment
12.4.3 Data Stored in EHR
12.4.4 Limitations
12.4.5 Future Scope
12.5 Conclusion
References
13. Smart Medication Dispensing: IoT Innovations in Drug Development
Sapna Singh Kshatri, Mukesh Kumar Chandrakar, Devanand Bhonsle, Manjushree Nayak, Prashant Tamrakar and Pramisha Sharma
13.1 Introduction
13.2 Problem Identification
13.3 Proposed Method
13.4 Applications
13.5 Use of ATMEGA328P Using Arduino
13.6 Software Used
13.7 Result and Discussion
13.8 Conclusion
References
14. Telemedicine and Virtual Health: Pioneering Innovation and Future Frontiers in Personalized Patient Care
R. Rahul, R. Raghul Jayaprakash, M. Shibhi Varmaah and S. Velmurugan
14.1 Introduction to Telemedicine and Virtual Health
14.1.1 Telemedicine
14.1.2 Virtual Health
14.1.3 Benefits of Telemedicine and Virtual Health
14.2 Challenges in Telemedicine
14.2.1 Reimbursements
14.2.2 Technical Issue
14.2.3 Privacy
14.2.4 Regulations
14.3 Artificial Intelligence in Telemedicine
14.3.1 Risk Prediction and Stratification
14.3.2 Personalized Treatment Planning
14.3.3 Virtual Assistants and Chatbots
14.4 Neurofeedback and Brain–Computer Interfaces (BCIs) in Telemedicine
14.4.1 Understanding Neurofeedback and BCIs
14.4.2 Integration of Neurofeedback and BCIs into Telemedicine Platforms
14.4.2.1 Software Development
14.4.2.2 Device Compatibility
14.4.3 Remote Neurofeedback Sessions: Accessibility and Convenience
14.4.3.1 Geographical Accessibility
14.4.3.2 Reduced Travel Burden
14.4.4 Challenges in Telemedicine Implementation
14.4.5 Opportunities in Telemedicine Implementation
14.5 Blockchain Technology in Virtual Healthcare
14.5.1 The Role of Blockchain in Virtual Healthcare
14.5.2 Enhancing Virtual Health Practices with Blockchain
14.6 Telemedicine for Personalized Patient Care
14.6.1 The Personalized Touch of Telemedicine
14.6.2 Personalized Telemedicine Services
14.7 Future Directions of Telemedicine in Healthcare
14.7.1 Virtual Reality and Augmented Reality
14.7.2 Remote Patient Management
References
15. Blockchain Algorithm: Revolutionizing Healthcare Systems
Ritika Awasthi and Arvind Tiwari
15.1 Introduction
15.2 How Blockchain can Relate to Healthcare
15.3 Literature Review
15.4 Features of Blockchain
15.4.1 Decentralized Storage
15.4.2 Immutability
15.4.3 Transparency
15.4.4 Privacy and Reliability
15.4.5 Security
15.5 Blockchain Algorithms
15.5.1 POA (Proof-of-Authority) Algorithm
15.5.2 Hyperledger Fabric Consensus
15.5.3 Squirrel Search Optimization Algorithm
15.6 Network Model in Blockchain Algorithm
15.6.1 Permissioned Blockchain
15.6.2 Consortium Block
15.6.3 Applications in Healthcare
15.6.3.1 Management of the Supply Chain
15.6.3.2 Improving Supply Chain Efficiency
15.6.3.3 Research and Clinical Trials
15.7 Data Collection and Storage
15.8 Diversity in Blockchain Technology
15.9 Limitations of Blockchain
15.9.1 Scalability Problems
15.9.2 Regulatory Difficulties
15.9.3 Data Privacy Issues
15.10 Conclusion
15.11 Future Work
References
16. Enhancing Cyber-Physical System Security in Healthcare Through Ensemble Learning, Blockchain and Multi-Attribute Feature Selection
Jagdish Pimple and Avinash Sharma
16.1 Introduction
16.1.1 Ensemble Techniques
16.1.1.1 Bagging or Bootstrap Aggregating
16.1.1.2 Random Forest Models
16.1.1.3 Stacking
16.1.2 Blockchain Overview
16.1.3 Blockchain Technology and Security of Health Data
16.1.4 A Health Care Blockchain Model
16.1.4.1 Scalability
16.1.4.2 Data Privacy and Access Security
16.2 Literature Survey
16.2.1 Comparative Analysis of the Above Literature Survey
16.3 Identification of the Problem
16.4 Objectives
16.5 Proposed Methodology
16.5.1 The Model-Building Procedure Based on the DCNN and Bag of Features
16.6 Result and Discussion
16.7 Conclusion and Future Work
References
17. Digitizing Wellness: A Deep Dive Into EHR/EMR Systems
Parul Dubey, Anansingh Thinakaran and Rajendra Motiramji Rewatkar
17.1 Introduction
17.2 Literature Review
17.3 AWS and Healthcare Solutions
17.4 AWS Services for Healthcare
17.5 Building EHR/EMR Solutions on AWS
17.6 Innovating with AI and Analytics
17.7 Case Studies
17.8 Proposed Architecture Overview
17.9 Conclusion
References
18. Harmony in Healthcare: Implementing an AI-Powered Biometric System
S. Sharmila, M. Nirmala, Somasundaram Devaraj and M. Menagadevi
18.1 Introduction to Biometric System
18.2 Types of Biometric Systems
18.3 Biometrics in Healthcare Application
18.4 Biometric System for Monitoring and Disease Diagnosis
18.5 Future Direction of Biometrics in Personalized Care
References
19. Investigating the Revolution of Healthcare Application with Intense Comparisons and Case Study
Amudhavalli P., S. Urmela, Vishnupriya G., N. Gopinath, R. Anandh and Lyndsay R. Buckingham
19.1 Introduction
19.2 Digital Twin
19.2.1 Digital Twin in Healthcare
19.2.2 Implementing DT in the Healthcare Sector
19.2.3 Components Related to Digital Twin
19.3 Case Study—Healthcare Applications
19.4 Future Research Ideas
19.5 Conclusion
References
Index
Back to Top
Preface
1. Introduction to Algorithmic Health: Exploring Healthcare Through Digital Twins
A.S. Vinay Raj, N. Gopinath, R. Anandh, M. Mohammed Jalaluddin and Lyndsay R. Buckingham
1.1 Introduction
1.2 Related Works
1.2.1 Reviews on Wearable Sensors
1.3 Hardware Description
1.3.1 Sensor Board
1.3.2 Power Requirements
1.3.3 ECG Sensor Measurements
1.3.4 PPG Sensor
1.3.5 Power Consumption
1.4 Methodology
1.4.1 Healthcare Monitoring System
1.4.2 Heart Rate Monitoring with ECG
1.4.3 Heart Rate Analysis
1.4.4 BP Analysis
1.4.5 Human Body Temperature Analysis
1.4.6 IoT-Based Remote Monitoring System
1.4.7 Network Connectivity
1.4.8 Network Gateway
1.4.9 Server
1.4.10 Database
1.4.11 Security
1.5 Performance Analysis
1.6 Conclusion
References
2. The Digital Revolution in Healthcare
Devanand Bhonsle, Rama Shukla, Deepshikha Sahu, Tanuja Kashyap, Monika Dewangan and Seema Mishra
2.1 Introduction
2.1.1 Challenges
2.1.2 Motivation
2.2 Digital Technologies in the Healthcare Sector
2.2.1 Electronic Health Record
2.2.2 Remote Patient Monitoring
2.2.3 Artificial Intelligence
2.2.4 Telemedicine
2.2.5 Federated Learning
2.3 Evolution of Digitalization in Business
2.3.1 Industry 4.0 and Business Intelligence
2.3.2 New Opportunities to Traditional Management
2.3.3 Role of Digitalization in Healthcare
2.3.4 Industry 4.0 in Healthcare Sector
2.4 Role of IoMT in Healthcare
2.4.1 Internet of Medical Things Architecture
2.5 Internet of Medical Things Devices
2.5.1 Wearable Devices
2.5.2 Smart Medical Devices
2.5.3 Telemedicine Devices
2.5.4 Smart Home Devices
2.6 Security and Privacy in the Healthcare Sector
2.7 Eliminating Security and Privacy Concerns of Digitalization of the Healthcare Sector
2.7.1 Threats to the Healthcare Sector
2.7.2 Developing Medical Equipment Management Programs
2.7.3 How are Medical Data Stored in Software Like EHR?
2.8 Discussion
2.9 Future Works
2.9.1 Research on Privacy and Security in the Healthcare Sector for the Better
2.9.2 Understanding the Impact of Digitization
2.9.3 Investigation of Privacy and Security Risks Associated with Wearable Technology
2.9.4 Analysis of Role of AI in Healthcare
2.9.5 Understand the Role of Block Chain Technology in Healthcare
2.9.6 Study on the Role of Patient Engagement in Privacy and Security
2.10 Conclusion
References
3. Data-Driven Diagnostics: Deep Learning for Brain Tumor Classification
Astha Pathak and Lalita Panika
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.4 Result Analysis
3.5 Conclusion
References
4. Predictive Analysis in Patient Care
Bolukonda Prashanth, Bandi Krishna, Rakesh Nayak, Umashankar Ghugar and Arunakranthi Godishala
4.1 Introduction
4.2 Review of Predictive Analysis
4.3 Conclusion and Future
References
5. Leveraging Predictive Analytics: Enhancing Brain Tumor Classification with XGBoost
Katakam Hemanvitha and Vikram Dhiman
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.4 Results and Discussion
5.5 Conclusion
References
6. Machine Learning in Medical Imaging Revolutionizing Lung Cancer Diagnosis: A Comparative Analysis of Convolutional Neural Networks and Vision Transformers in Medical Imaging
Priya Parkhi, Bhagyashree Hambarde, Himesh Gangwani, Rupali Vairagade and Fred Kalombo
6.1 Introduction
6.2 Literature Review
6.3 Description of Model
6.4 Methodology
6.5 Results
6.6 Conclusion
References
7. Innovations in AI and ML for Medical Imaging: An Extensive Study with an Emphasis on Face Spoofing Detection and Snooping
Aparna Pandey, Arvind Kumar Tiwari, Harsha Nishad and Siji A. Thomas
7.1 Introduction
7.2 Artificial Intelligence as Well as Device Understandings
7.3 Assaults Through Entrance Spoofing
7.4 A Case Study with Real-Time Narrative: Identifying Face Spoofing in Medical Imaging
7.5 Moral Factors to Consider
7.6 Discussion
7.7 Summary
References
8. Progressive Growing of Generative Adversarial Networks (PGGAN) Approach to Synthesize Medical Images
Vishal V. Raner, Amit D. Joshi, Suraj T. Sawant and Tamizharasan P. S.
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 Results and Discussion
8.5 Conclusions
Acknowledgments
References
9. Revolutionizing Healthcare Through Optimized Video Retrieval
Pratibha Singh and Alok Kumar Singh Kushwaha
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.3.1 Dataset
9.4 Results and Discussion
9.5 Conclusion
References
10. Multiclass Classification of Oral Diseases Using Deep Learning Models
Mohammed Zubair Hussain, Shrey Gupta, Bhagyashree Hambarde, Priya Parkhi and Zafar Karimov
10.1 Introduction
10.2 Literature Review
10.3 Methodology
10.4 Results
10.5 Conclusion
References
11. Smart Wearable Devices for Remote Patient Monitoring in Healthcare
Ravi Mishra, Swati Chaitandas Hadke, Devanand Bhonsle, Priti Nilesh Bhagat, Anupama Mahabansi and Sheetal Mungale
11.1 Introduction
11.2 Wearable Devices for Remote Monitoring
11.3 Communication Technologies for Remote Healthcare Monitoring
11.4 Proposed Methodology
11.5 Conclusion
References
12. Efficient IoT Solutions for Remote Health Monitoring
Vijayakumar S., N. Sheik Hameed, Kanchan S. Tiwari, A. Allwyn Sundarraj, N. Gopinath and Lyndsay R. Buckingham
12.1 Introduction
12.1.1 Introduction to DT
12.1.2 Digital Twin in the Healthcare Sector
12.2 Related Works
12.3 Methodology
12.3.1 IoT in Healthcare
12.3.2 IoT in Digital Healthcare
12.3.3 Internet of Medical Things (IoMT) Devices
12.3.4 Security and Privacy Solutions
12.4 Discussion
12.4.1 Risks to the Healthcare Industry
12.4.2 Creating Management Programs for Medical Equipment
12.4.3 Data Stored in EHR
12.4.4 Limitations
12.4.5 Future Scope
12.5 Conclusion
References
13. Smart Medication Dispensing: IoT Innovations in Drug Development
Sapna Singh Kshatri, Mukesh Kumar Chandrakar, Devanand Bhonsle, Manjushree Nayak, Prashant Tamrakar and Pramisha Sharma
13.1 Introduction
13.2 Problem Identification
13.3 Proposed Method
13.4 Applications
13.5 Use of ATMEGA328P Using Arduino
13.6 Software Used
13.7 Result and Discussion
13.8 Conclusion
References
14. Telemedicine and Virtual Health: Pioneering Innovation and Future Frontiers in Personalized Patient Care
R. Rahul, R. Raghul Jayaprakash, M. Shibhi Varmaah and S. Velmurugan
14.1 Introduction to Telemedicine and Virtual Health
14.1.1 Telemedicine
14.1.2 Virtual Health
14.1.3 Benefits of Telemedicine and Virtual Health
14.2 Challenges in Telemedicine
14.2.1 Reimbursements
14.2.2 Technical Issue
14.2.3 Privacy
14.2.4 Regulations
14.3 Artificial Intelligence in Telemedicine
14.3.1 Risk Prediction and Stratification
14.3.2 Personalized Treatment Planning
14.3.3 Virtual Assistants and Chatbots
14.4 Neurofeedback and Brain–Computer Interfaces (BCIs) in Telemedicine
14.4.1 Understanding Neurofeedback and BCIs
14.4.2 Integration of Neurofeedback and BCIs into Telemedicine Platforms
14.4.2.1 Software Development
14.4.2.2 Device Compatibility
14.4.3 Remote Neurofeedback Sessions: Accessibility and Convenience
14.4.3.1 Geographical Accessibility
14.4.3.2 Reduced Travel Burden
14.4.4 Challenges in Telemedicine Implementation
14.4.5 Opportunities in Telemedicine Implementation
14.5 Blockchain Technology in Virtual Healthcare
14.5.1 The Role of Blockchain in Virtual Healthcare
14.5.2 Enhancing Virtual Health Practices with Blockchain
14.6 Telemedicine for Personalized Patient Care
14.6.1 The Personalized Touch of Telemedicine
14.6.2 Personalized Telemedicine Services
14.7 Future Directions of Telemedicine in Healthcare
14.7.1 Virtual Reality and Augmented Reality
14.7.2 Remote Patient Management
References
15. Blockchain Algorithm: Revolutionizing Healthcare Systems
Ritika Awasthi and Arvind Tiwari
15.1 Introduction
15.2 How Blockchain can Relate to Healthcare
15.3 Literature Review
15.4 Features of Blockchain
15.4.1 Decentralized Storage
15.4.2 Immutability
15.4.3 Transparency
15.4.4 Privacy and Reliability
15.4.5 Security
15.5 Blockchain Algorithms
15.5.1 POA (Proof-of-Authority) Algorithm
15.5.2 Hyperledger Fabric Consensus
15.5.3 Squirrel Search Optimization Algorithm
15.6 Network Model in Blockchain Algorithm
15.6.1 Permissioned Blockchain
15.6.2 Consortium Block
15.6.3 Applications in Healthcare
15.6.3.1 Management of the Supply Chain
15.6.3.2 Improving Supply Chain Efficiency
15.6.3.3 Research and Clinical Trials
15.7 Data Collection and Storage
15.8 Diversity in Blockchain Technology
15.9 Limitations of Blockchain
15.9.1 Scalability Problems
15.9.2 Regulatory Difficulties
15.9.3 Data Privacy Issues
15.10 Conclusion
15.11 Future Work
References
16. Enhancing Cyber-Physical System Security in Healthcare Through Ensemble Learning, Blockchain and Multi-Attribute Feature Selection
Jagdish Pimple and Avinash Sharma
16.1 Introduction
16.1.1 Ensemble Techniques
16.1.1.1 Bagging or Bootstrap Aggregating
16.1.1.2 Random Forest Models
16.1.1.3 Stacking
16.1.2 Blockchain Overview
16.1.3 Blockchain Technology and Security of Health Data
16.1.4 A Health Care Blockchain Model
16.1.4.1 Scalability
16.1.4.2 Data Privacy and Access Security
16.2 Literature Survey
16.2.1 Comparative Analysis of the Above Literature Survey
16.3 Identification of the Problem
16.4 Objectives
16.5 Proposed Methodology
16.5.1 The Model-Building Procedure Based on the DCNN and Bag of Features
16.6 Result and Discussion
16.7 Conclusion and Future Work
References
17. Digitizing Wellness: A Deep Dive Into EHR/EMR Systems
Parul Dubey, Anansingh Thinakaran and Rajendra Motiramji Rewatkar
17.1 Introduction
17.2 Literature Review
17.3 AWS and Healthcare Solutions
17.4 AWS Services for Healthcare
17.5 Building EHR/EMR Solutions on AWS
17.6 Innovating with AI and Analytics
17.7 Case Studies
17.8 Proposed Architecture Overview
17.9 Conclusion
References
18. Harmony in Healthcare: Implementing an AI-Powered Biometric System
S. Sharmila, M. Nirmala, Somasundaram Devaraj and M. Menagadevi
18.1 Introduction to Biometric System
18.2 Types of Biometric Systems
18.3 Biometrics in Healthcare Application
18.4 Biometric System for Monitoring and Disease Diagnosis
18.5 Future Direction of Biometrics in Personalized Care
References
19. Investigating the Revolution of Healthcare Application with Intense Comparisons and Case Study
Amudhavalli P., S. Urmela, Vishnupriya G., N. Gopinath, R. Anandh and Lyndsay R. Buckingham
19.1 Introduction
19.2 Digital Twin
19.2.1 Digital Twin in Healthcare
19.2.2 Implementing DT in the Healthcare Sector
19.2.3 Components Related to Digital Twin
19.3 Case Study—Healthcare Applications
19.4 Future Research Ideas
19.5 Conclusion
References
Index
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