Healthcare Frontiers in the Metaverse
Innovations and Impacts
- 1st Edition - July 31, 2025
- Imprint: Academic Press
- Editor: Shalli Rani
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 2 9 9 8 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 2 9 9 9 - 9
Healthcare Frontiers in the Metaverse: Innovations and Impacts provides a deeper comprehension of the metaverse’s revolutionary effects on patient care, medical education, a… Read more
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Healthcare Frontiers in the Metaverse: Innovations and Impacts provides a deeper comprehension of the metaverse’s revolutionary effects on patient care, medical education, and operational procedures in the healthcare sector. This book Includes both the fundamentals of patient experience, provider practices, and the overall framework of the healthcare business as the democratization of medical education through virtual simulations and the individualized care enabled by AI-driven diagnostics. In 14 chapters, Healthcare Frontiers in the Metaverse: Innovations and Impacts leads the reader to an introduction to definition, elements, treatment and diagnostic in healthcare in metaverse, the next evolution in social connection, and the successor to the mobile internet. It describes the role of robots and give a view on the future scope and challenges. Healthcare Frontiers in the Metaverse: Innovations and Impacts serves as a road map for technologists and policymakers influencing the direction of healthcare as well as a help book for medical practitioners integrating metaverse technologies into their practice.
• Investigates the dynamic and transformative synergy that exists between the metaverse and healthcare
• Offers a thorough examination of the opportunities, difficulties, and revolutionary potential that arise from the convergence of cutting-edge metaverse technology with the healthcare sector
• Explores the ways in which the metaverse is changing patient care, medical education, research, and the landscape of healthcare in general, from virtual reality therapy to AI-driven diagnostics
• Offers a thorough examination of the opportunities, difficulties, and revolutionary potential that arise from the convergence of cutting-edge metaverse technology with the healthcare sector
• Explores the ways in which the metaverse is changing patient care, medical education, research, and the landscape of healthcare in general, from virtual reality therapy to AI-driven diagnostics
Researchers, academics and advanced level students in artificial intelligence and health care
List of contributors
About the editor
Preface
Acknowledgments
Chapter 1: Blockchain technology and the metaverse: revolutionizing healthcare data security and efficiency
Vandana, Chetna Sharma, and Md. Mehedi Hassan
1.1 Introduction
1.1.1 How can it be used to enhance the healthcare industry?
1.2 Recent studies in healthcare
1.3 Blockchain technology facilitators for the revival of healthcare services in pandemic
1.3.1 Role of Blockchain in COVID-19 detection and prevention
1.3.2 Role of blockchain in monkeypox identification
1.4 Conclusion and future scope
References
Chapter 2: MedTech Fusion in the Metaverse using business intelligence through AIoTBc: artificial intelligence, Internet of Things, blockchain
Ahmed Mateen Buttar, Muhammad Anwar Shahid, Sadaf Naz, and Muhammad Azeem Akbar
2.1 Introduction
2.1.1 Background and motivation
2.1.2 Overview of the Metaverse and healthcare integration
2.1.3 Objectives
2.1.4 Problem statement
2.2 Internet of Things in healthcare
2.2.1 Real-time monitoring and data collection
2.2.2 Implementation of smart inhalers for asthma patients
2.2.3 Enhancing patient care with interconnected devices
2.3 Federated Learning in medical research
2.3.1 Preserving data privacy and security
2.3.2 Benefits of preserving data privacy
2.3.3 Potential challenges
2.3.4 Case study of predictive model for sepsis detection
2.4 Results
2.5 Explainable artificial intelligence in healthcare
2.5.1 Importance of transparency and trust
2.5.2 Developing actionable treatment plans
2.6 Blockchain technology in healthcare
2.6.1 Safeguarding health information privacy
2.6.2 Applications in healthcare
2.6.3 Implementation of blockchain for managing electronic health records in a healthcare consortium
2.6.4 Promoting data sharing and security
2.7 Case studies on technology implementation
2.7.1 Virtual hospital in the Metaverse: Internet of Things and telemedicine
2.8 Ethical considerations and best practices
2.8.1 Addressing ethical dilemmas in higher learning institutions
2.8.2 Data privacy and security
2.8.3 Distribution of healthcare resources
2.9 Future directions and research opportunities
2.9.1 Potential benefits of leveraging technologies in the metaverse
2.9.2 Areas for future research and development
2.10 Conclusion
References
Chapter 3: Intelligent medical report extraction and generation model for smart healthcare services
Mohammad Shabaz, S. Anuradha, Mohan Raparthi, Maher Ali Rusho, and Mukesh Soni
3.1 Introduction
3.2 Related work
3.2.1 Summary generation
3.2.2 Generation of medical reports
3.3 Methods
3.3.1 Task definition
3.3.2 Model framework
3.3.3 Key sentence extraction
3.3.4 Report generation
3.3.5 Model loss function
3.4 Experimental results and analysis
3.4.1 Experimental settings
3.4.2 Baseline models
3.4.3 Experimental results and analysis
3.4.4 Sensitivity analysis
3.5 Conclusion
References
Chapter 4: Blockchain aware game theory-based medical data sharing scheme
Ramswaroop Reddy Yellu, Pramod Kumar, Mohammed Wasim Bhatt, Ismail Keshta, and Mukesh Soni
4.1 Introduction
4.2 Blockchain-based system for the exchange of health records
4.3 Game-based approach to sharing health records
4.3.1 Model assumptions
4.3.2 Construction of payoff matrix
4.4 Model stability analysis
4.4.1 Replication dynamic equation of the system management party
4.4.2 Replication dynamic equation of data provider
4.4.3 Dynamics equation of data demanders’ replication
4.4.4 Stability analysis of three-party evolutionary game
4.5 Numerical simulation and study
4.5.1 Evolutionary paths of three-party game behavior
4.5.2 The effect of starting strategies on the reliability of game plans
4.5.3 Influence of different factors on the strategy selection of stakeholders
4.6 Conclusion and recommendations
4.6.1 Conclusion
4.6.2 Recommendations
References
Chapter 5: Real-time brain monitoring: application of wearable neurotech in healthcare
P.T. Garima Wadhwa
5.1 Introduction to wearable neurotechnology
5.1.1 Neuroimaging
5.1.2 Neuromodulation
5.1.3 Brain computer interface
5.1.4 Neurofeedback
5.2 Salient features of wearable neurotechnology, devices, and mechanisms
5.2.1 Electroencephalography
5.2.2 Functional near-infrared spectroscopy
5.2.3 Magnetoencephalography
5.3 Application of wearable neurotechnology in healthcare
5.3.1 Wearable neurotechnology for real-time brain monitoring and diagnostics
5.3.2 Neurofeedback as therapeutic intervention in neurorehabilitation
5.3.3 Wearable neurotechnology in research and development
5.3.4 Wearable neurotechnology in telehealth
5.4 Integration of wearable neurotechnology with metaverse
5.5 Challenges and future directions in wearable neurotechnology
5.5.1 Future directions
References
Chapter 6: Predictive analytics and diagnostics in personalized medicine using machine learning models
Sarthak Sharma, Gurwinder Singh, and Vyasa Sai
6.1 Introduction
6.2 Foundation of machine learning in healthcare
6.2.1 Data sources and preprocessing techniques
6.3 Applications of machine learning models in predictive analytics
6.4 Integration with metaverse technologies
6.4.1 Virtual simulations for patient monitoring and diagnosis
6.5 Predictive analytics with machine learning models
6.5.1 Understanding data
6.5.2 Feature selection
6.5.3 Machine learning models
6.6 Case study: personalized treatment plans for diabetes management
6.6.1 Statistical data
6.6.2 Analysis: virtual reality applications for diabetes management
6.7 Challenges and future directions
6.7.1 Technical challenges in implementing machine learning models
6.7.2 Future trends in predictive analytics and diagnostics
6.7.3 The role of emerging technologies in shaping personalized medicine
6.8 Conclusion
References
Chapter 7: Ethical research: the role of robotics in healthcare and metaverse’s legal considerations
G. Vennira Selvi, G. Ramani, K. Meena, and G. JothiPriya
7.1 Introduction
7.1.1 Evolution of robotics
7.1.2 Importance of ethical considerations
7.1.3 Legal and regulatory landscape
7.1.4 Structure of the chapter
7.2 Fundamentals of roboethics
7.2.1 Ethical design and development
7.2.2 Human-robot interaction
7.2.3 Privacy and data protection
7.2.4 Accountability and liability
7.2.5 Ethical decision-making by robots
7.3 Ethical principles in robotics research
7.3.1 Transparency and explainability
7.3.2 Accountability and responsibility
7.3.3 Fairness and equity
7.3.4 Human-robot interaction and safety
7.4 Societal impact and responsibility of robotics
7.4.1 Improving efficiency and productivity
7.4.2 Addressing societal challenges
7.4.3 Impact on employment and workforce dynamics
7.4.4 Ethical considerations and social acceptance
7.5 Cultivating inclusivity and accessibility
7.6 Case studies in ethical robotics
7.6.1 Healthcare robotics: assistive technologies and patient care
7.6.2 Autonomous vehicles: safety, liability, and decision-making
7.6.3 Robotics in warfare: ethical considerations in military applications
7.6.4 Ethical challenges in human-robot interaction
7.6.5 Ethical decision-making in autonomous systems
7.7 Legal frameworks and regulations in robotics
7.7.1 International standards and guidelines
7.7.2 National regulations and legislation
7.7.3 Intellectual property rights and patents
7.7.4 Data protection and privacy laws
7.7.5 Safety standards and certification
7.7.6 Ethical guidelines and best practices
7.8 Challenges and ethical dilemmas in robotics
7.8.1 Human-robot interaction challenges
7.8.2 Ethical use of artificial intelligence in robotics
7.8.3 Privacy and data security concerns
7.8.4 Employment displacement and economic impacts
7.8.5 Ethical decision-making in autonomous systems
7.9 Future directions and emerging issues in robotics
7.9.1 Advancements in artificial intelligence and machine learning
7.9.2 Human-robot collaboration and coexistence
7.9.3 Autonomous systems and mobility
7.9.4 Ethical and regulatory challenges
7.9.5 Sustainability and environmental impact
7.10 Types of robotics and their applications
7.11 Moral responsibility of robots
7.12 The interaction between robot and human
7.12.1 Technological aspects of human-robot interaction
7.12.2 Socio-psychological aspects of human-robot interaction
7.12.3 Future directions in human-robot interaction
7.13 Ethical problems of using medical robots
7.14 Criminal behavior of robots
7.15 Conclusion
References
Chapter 8: Metaverse-enhanced deep neural network approach for detecting and classifying catheters in chest X-rays
Agam Madan, Ankit Chaudhary, Vedika Gupta, and Narayan Ranjan Chakraborty
8.1 Introduction
8.2 Related work
8.3 Data
8.4 Method
8.4.1 Preprocessing
8.4.2 Transfer learning
8.4.3 EfficientNet B7
8.4.4 Training and fine-tuning
8.5 Experimental results
8.6 Evaluation
8.7 Conclusion
References
Chapter 9: Case study, future scope, and vision of metaverse in diagnosing brain tumor disease
Kamini Lamba and Shalli Rani
9.1 Introduction
9.1.1 Evolution and background of the metaverse
9.1.2 Evolution and background of brain tumor disease
9.1.3 Integration of metaverse in brain tumor diagnosis and treatment
9.1.4 Components of metaverse
9.2 Existing work
9.3 Case studies
9.4 Future scope of metaverse in diagnosing brain tumors
9.4.1 Technological advancements
9.4.2 Personalized medicine
9.4.3 Global collaboration
9.4.4 Integration with emerging technologies
9.5 Vision for the future
9.5.1 Integration of advanced technologies: comprehensive diagnostic platforms
9.5.2 Patient-centric care: personalized treatment plans
9.5.3 Enhanced medical education and training: immersive training environments
9.5.4 Robust global healthcare networks: global collaboration
9.5.5 Ethical and regulatory considerations: ensuring ethical practices
9.6 Integration of future scope and vision of metaverse technologies in diagnosing brain tumor diseases
9.6.1 Technological advancements
9.6.2 Integration with healthcare systems
9.6.3 Global accessibility and impact
References
Further reading
Chapter 10: Robotic revolution: ethical and legal frontiers in healthcare metaverse research
Azamat Ali
10.1 Introduction
10.2 The role of robotics in healthcare research
10.2.1 Robotics in diagnostics
10.2.2 Robotics in treatment
10.2.3 Robotics in surgery
10.2.4 Robotics in patient care
10.3 Ethical considerations in robotics research
10.3.1 Patient autonomy in virtual healthcare interactions
10.3.2 Privacy and data security challenges
10.3.3 Algorithmic bias and fairness
10.3.4 Implications for patient well-being and healthcare equity
10.4 Legal frameworks governing robotics in healthcare
10.4.1 Medical device regulations
10.4.2 Product liability laws
10.4.3 Data protection regulations
10.4.4 Intellectual property rights
10.4.5 Analysis of liability issues
10.4.6 Regulatory compliance challenges
10.5 Navigating ethical and legal challenges in robotics research
10.5.1 Strategies for addressing ethical dilemmas in robotics research
10.6 Recommendations for developing ethical guidelines specific to healthcare in the metaverse
10.6.1 Suggestions for navigating legal complexities and ensuring compliance with regulations
10.7 Case studies illustrating ethical and legal challenges in robotics research
10.7.1 Case study 1: Da Vinci Surgical System and patient safety
10.7.2 Case study 2: Ethical use of artificial intelligence in diagnostic robotics
10.7.3 Case study 3: Regulatory compliance and telemedicine robotics
10.7.4 Case study 4: Robotics in elderly care and ethical considerations
10.8 Conclusion and future directions
References
Chapter 11: Adaptive virtual reality exposure therapy and motor rehabilitation from Hebbian learning rule in metaverse: (Δ W/ij) for psychoanalysis
Akey Sungheetha, R. Rajesh Sharma, John Blake, and B.P. Sreeja
11.1 Introduction
11.2 Literature study analysis
11.2.1 Virtual reality in exposure therapy
11.2.2 Handling pain by virtual reality
11.2.3 Virtual reality in motor rehabilitation
11.2.4 Challenges and limitations
11.2.5 Emerging trends and future directions
11.3 Methodology
11.3.1 Adaptive virtual reality exposure therapy system
11.3.2 Flowchart of the adaptive virtual reality exposure therapy system
11.3.3 VR-based motor rehabilitation system
11.3.4 Flowchart of the virtual reality-based motor rehabilitation system
11.4 Results and discussion
11.4.1 Experimental setup
11.4.2 Performance metrics
11.4.3 Results
11.4.4 Discussion
11.5 Conclusion
References
Further reading
Chapter 12: Implementing virtual patient simulation and surgical training in medical school: finding Data-S and Learning-R
Akey Sungheetha, R. Rajesh Sharma, Oluwasegun Julius Aroba, and B.P. Sreeja
12.1 Introduction
12.2 Literature study analysis
12.2.1 Virtual patient simulations
12.2.2 Virtual surgical training
12.2.3 Immersive anatomical education
12.2.4 Challenges and limitations
12.2.5 Emerging trends and future directions
12.3 Methodology
12.3.1 Adaptive virtual patient simulation system
12.3.2 Collaborative virtual surgical training platform
12.4 Results and discussion
12.4.1 Experimental setup
12.4.2 Performance metrics
12.4.3 Discussion
12.4.4 Collaborative virtual surgical training performance
12.4.5 Engagement and knowledge retention
12.4.6 Limitations and future work
12.5 Conclusion
References
Further reading
Chapter 13: Healthcare frontiers in the metaverse: innovations and impacts
Nikita Sharma, Kriti Sankhla, Hemlata Jain, and Ajay Khunteta
13.1 Introduction
13.1.1 Summary of the metaverse
13.1.2 Healthcare’s evolution
13.1.3 The scene of the metaverse
13.2 Definition and elements
13.2.1 Definition of the metaverse
13.2.2 Core elements of the metaverse
13.2.3 New technologies: the revolution in digital health
13.3 Wearables and health monitoring
13.3.1 Virtual reality in healthcare
13.3.2 Telehealth in the metaverse
13.4 Virtual reality treatments
13.4.1 Simulated surgery
13.4.2 Medical artificial intelligence and machine learning
13.5 Predictive analytics and diagnostics in medicine
13.5.1 Data security and blockchain
13.6 Protecting patient confidentiality with open health records
13.6.1 Comprehensive medical education
13.7 Simulated training environments in virtual medical schools
13.7.1 Remote patient monitoring and telemedicine
13.8 Metaverse device connectivity
13.8.1 Real-time health monitoring
13.9 Online communities of support
13.9.1 Empowerment of patients
13.9.2 Platforms for mental health and well-being
13.10 The role of robotics
13.10.1 Robots for surgery
13.10.2 Artificial intelligence-powered assistive technology
13.11 Case study for research effective applications
13.11.1 Study 1: virtual reality-assisted surgical training
13.11.2 Study 2: virtual reality-based exposure therapy for anxiety disorders
13.11.3 Study 3: virtual reality for burn patients’ pain management
13.11.4 Study 4: virtual reality-based cognitive rehabilitation for stroke patients
13.12 Legal aspects for ethical research privacy issues
13.12.1 Fairness and availability in the metaverse’s legal environment
13.12.2 Handling legal obstacles
13.12.3 Standardization and conformance
13.13 Future scope and challenges
13.13.1 Anticipated research gaps and challenges
13.13.2 New trends and innovations
13.14 Vision of the metaverse in healthcare
References
Further reading
Chapter 14: Vision of the metaverse in healthcare
Shagun Arora and Jyoteesh Malhotra
14.1 Introduction
14.2 Materials and methods
14.2.1 Search strategy
14.2.2 Eligibility criteria
14.2.3 Data sources, study selection, and data extraction
14.3 Results
14.3.1 Characteristics of the included studies
14.4 Discussion
14.4.1 Metaverse for prevention and treatment
14.4.2 Metaverse and research application
14.4.3 Limitations of the metaverse
14.4.4 Challenges and open issues in metaverse healthcare
14.4.5 Future directions in metaverse healthcare
14.5 Conclusions
References
Chapter 15: Case study, future scope and vision of metaverse in healthcare
Priyanka Datta, Neha Garg, Amitesh Aggarwal, Amanpreet Kaur, and Yonis Gulzar
15.1 Introduction
15.1.1 History
15.1.2 Application of metaverse
15.1.3 Metaverse in healthcare
15.2 Case study
15.2.1 Case study 1: innerworld
15.2.2 Case study 2: Karuna Labs
15.2.3 Comparison of case studies
15.3 Future scope and limitations
15.4 Conclusion
References
Further reading
Chapter 16: The role of robotics: deep Q-network-based fusion for healthcare metaverse applications
Akey Sungheetha, R. Rajesh Sharma, Oluwasegun Julius Aroba, and B.P. Sreeja
16.1 Introduction
16.1.1 Key facets to recognize the functions of robotics
16.2 Literature study analysis
16.2.1 Robotic kinematics and dynamics
16.2.2 Control algorithms
16.2.3 Sensing and perception
16.2.4 Human-robot interaction
16.2.5 Challenges and future directions
16.2.6 Identified future research directions
16.3 Methodology
16.3.1 Overview of the proposed approach
16.4 Results and discussion
16.4.1 Experimental setup
16.4.2 Performance metrics
16.4.3 Discussion
16.5 Conclusion
References
Further reading
Chapter 17: Transforming healthcare in the metaverse with impact of wearable technology
Ankita Sharma and Shalli Rani
17.1 Introduction
17.2 Applications of healthcare in metaverse
17.2.1 Medical training
17.2.2 Virtual consultation
17.2.3 Remote monitoring and integration of wearables
17.2.4 Mental healthcare
17.2.5 Virtual wellness
17.3 Metaverse with the XR technology
17.3.1 Artificial intelligence for the metaverse in healthcare
17.4 Use of metaverse in healthcare
17.4.1 Use cases of metaverse in artificial intelligence
17.5 Security challenges in metaverse in healthcare
17.6 Conclusion
17.7 Future work
References
Chapter 18: Optimal diabetes prediction via synergistic hyperparameter tuning and Synthetic Minority Oversampling Technique techniques
Devanshi Gupta, Kanwarpartap Singh Gill, Rahul Chauhan, and Hemant Singh Pokhariya
18.1 Introduction
18.2 Literature
18.3 Input dataset
18.4 Proposed methodology
18.5 Results
18.5.1 Confusion matrix analysis
18.5.2 Tables and figures
18.6 Conclusion
18.7 Future scope
References
Further reading
Chapter 19: Raspberry Pi-enhanced health tracking for complete well-being solutions
Agampreet Singh, Kanwarpartap Singh Gill, Mukesh Kumar, and Ruchira Rawat
19.1 Introduction
19.2 Literature
19.3 Proposed methodology
19.4 Results
19.4.1 Improved e-health monitoring
19.4.2 Pulse acquisition module
19.4.3 Body temperature measuring component
19.4.4 Parametric readings percentage analysis
19.5 Conclusion
References
Chapter 20: Developers’ engineering of metaverse and healthcare procurements
Harbani Sharma, Sheena Angra, and Bhanu Sharma
20.1 Introduction
20.2 Related work
20.3 Immersive technologies
20.4 Metaverse-healthcare: a radical collaboration
20.5 Challenges of using metaverse in healthcare
20.6 Devices used in metaverse
20.7 Conclusion
References
Index
About the editor
Preface
Acknowledgments
Chapter 1: Blockchain technology and the metaverse: revolutionizing healthcare data security and efficiency
Vandana, Chetna Sharma, and Md. Mehedi Hassan
1.1 Introduction
1.1.1 How can it be used to enhance the healthcare industry?
1.2 Recent studies in healthcare
1.3 Blockchain technology facilitators for the revival of healthcare services in pandemic
1.3.1 Role of Blockchain in COVID-19 detection and prevention
1.3.2 Role of blockchain in monkeypox identification
1.4 Conclusion and future scope
References
Chapter 2: MedTech Fusion in the Metaverse using business intelligence through AIoTBc: artificial intelligence, Internet of Things, blockchain
Ahmed Mateen Buttar, Muhammad Anwar Shahid, Sadaf Naz, and Muhammad Azeem Akbar
2.1 Introduction
2.1.1 Background and motivation
2.1.2 Overview of the Metaverse and healthcare integration
2.1.3 Objectives
2.1.4 Problem statement
2.2 Internet of Things in healthcare
2.2.1 Real-time monitoring and data collection
2.2.2 Implementation of smart inhalers for asthma patients
2.2.3 Enhancing patient care with interconnected devices
2.3 Federated Learning in medical research
2.3.1 Preserving data privacy and security
2.3.2 Benefits of preserving data privacy
2.3.3 Potential challenges
2.3.4 Case study of predictive model for sepsis detection
2.4 Results
2.5 Explainable artificial intelligence in healthcare
2.5.1 Importance of transparency and trust
2.5.2 Developing actionable treatment plans
2.6 Blockchain technology in healthcare
2.6.1 Safeguarding health information privacy
2.6.2 Applications in healthcare
2.6.3 Implementation of blockchain for managing electronic health records in a healthcare consortium
2.6.4 Promoting data sharing and security
2.7 Case studies on technology implementation
2.7.1 Virtual hospital in the Metaverse: Internet of Things and telemedicine
2.8 Ethical considerations and best practices
2.8.1 Addressing ethical dilemmas in higher learning institutions
2.8.2 Data privacy and security
2.8.3 Distribution of healthcare resources
2.9 Future directions and research opportunities
2.9.1 Potential benefits of leveraging technologies in the metaverse
2.9.2 Areas for future research and development
2.10 Conclusion
References
Chapter 3: Intelligent medical report extraction and generation model for smart healthcare services
Mohammad Shabaz, S. Anuradha, Mohan Raparthi, Maher Ali Rusho, and Mukesh Soni
3.1 Introduction
3.2 Related work
3.2.1 Summary generation
3.2.2 Generation of medical reports
3.3 Methods
3.3.1 Task definition
3.3.2 Model framework
3.3.3 Key sentence extraction
3.3.4 Report generation
3.3.5 Model loss function
3.4 Experimental results and analysis
3.4.1 Experimental settings
3.4.2 Baseline models
3.4.3 Experimental results and analysis
3.4.4 Sensitivity analysis
3.5 Conclusion
References
Chapter 4: Blockchain aware game theory-based medical data sharing scheme
Ramswaroop Reddy Yellu, Pramod Kumar, Mohammed Wasim Bhatt, Ismail Keshta, and Mukesh Soni
4.1 Introduction
4.2 Blockchain-based system for the exchange of health records
4.3 Game-based approach to sharing health records
4.3.1 Model assumptions
4.3.2 Construction of payoff matrix
4.4 Model stability analysis
4.4.1 Replication dynamic equation of the system management party
4.4.2 Replication dynamic equation of data provider
4.4.3 Dynamics equation of data demanders’ replication
4.4.4 Stability analysis of three-party evolutionary game
4.5 Numerical simulation and study
4.5.1 Evolutionary paths of three-party game behavior
4.5.2 The effect of starting strategies on the reliability of game plans
4.5.3 Influence of different factors on the strategy selection of stakeholders
4.6 Conclusion and recommendations
4.6.1 Conclusion
4.6.2 Recommendations
References
Chapter 5: Real-time brain monitoring: application of wearable neurotech in healthcare
P.T. Garima Wadhwa
5.1 Introduction to wearable neurotechnology
5.1.1 Neuroimaging
5.1.2 Neuromodulation
5.1.3 Brain computer interface
5.1.4 Neurofeedback
5.2 Salient features of wearable neurotechnology, devices, and mechanisms
5.2.1 Electroencephalography
5.2.2 Functional near-infrared spectroscopy
5.2.3 Magnetoencephalography
5.3 Application of wearable neurotechnology in healthcare
5.3.1 Wearable neurotechnology for real-time brain monitoring and diagnostics
5.3.2 Neurofeedback as therapeutic intervention in neurorehabilitation
5.3.3 Wearable neurotechnology in research and development
5.3.4 Wearable neurotechnology in telehealth
5.4 Integration of wearable neurotechnology with metaverse
5.5 Challenges and future directions in wearable neurotechnology
5.5.1 Future directions
References
Chapter 6: Predictive analytics and diagnostics in personalized medicine using machine learning models
Sarthak Sharma, Gurwinder Singh, and Vyasa Sai
6.1 Introduction
6.2 Foundation of machine learning in healthcare
6.2.1 Data sources and preprocessing techniques
6.3 Applications of machine learning models in predictive analytics
6.4 Integration with metaverse technologies
6.4.1 Virtual simulations for patient monitoring and diagnosis
6.5 Predictive analytics with machine learning models
6.5.1 Understanding data
6.5.2 Feature selection
6.5.3 Machine learning models
6.6 Case study: personalized treatment plans for diabetes management
6.6.1 Statistical data
6.6.2 Analysis: virtual reality applications for diabetes management
6.7 Challenges and future directions
6.7.1 Technical challenges in implementing machine learning models
6.7.2 Future trends in predictive analytics and diagnostics
6.7.3 The role of emerging technologies in shaping personalized medicine
6.8 Conclusion
References
Chapter 7: Ethical research: the role of robotics in healthcare and metaverse’s legal considerations
G. Vennira Selvi, G. Ramani, K. Meena, and G. JothiPriya
7.1 Introduction
7.1.1 Evolution of robotics
7.1.2 Importance of ethical considerations
7.1.3 Legal and regulatory landscape
7.1.4 Structure of the chapter
7.2 Fundamentals of roboethics
7.2.1 Ethical design and development
7.2.2 Human-robot interaction
7.2.3 Privacy and data protection
7.2.4 Accountability and liability
7.2.5 Ethical decision-making by robots
7.3 Ethical principles in robotics research
7.3.1 Transparency and explainability
7.3.2 Accountability and responsibility
7.3.3 Fairness and equity
7.3.4 Human-robot interaction and safety
7.4 Societal impact and responsibility of robotics
7.4.1 Improving efficiency and productivity
7.4.2 Addressing societal challenges
7.4.3 Impact on employment and workforce dynamics
7.4.4 Ethical considerations and social acceptance
7.5 Cultivating inclusivity and accessibility
7.6 Case studies in ethical robotics
7.6.1 Healthcare robotics: assistive technologies and patient care
7.6.2 Autonomous vehicles: safety, liability, and decision-making
7.6.3 Robotics in warfare: ethical considerations in military applications
7.6.4 Ethical challenges in human-robot interaction
7.6.5 Ethical decision-making in autonomous systems
7.7 Legal frameworks and regulations in robotics
7.7.1 International standards and guidelines
7.7.2 National regulations and legislation
7.7.3 Intellectual property rights and patents
7.7.4 Data protection and privacy laws
7.7.5 Safety standards and certification
7.7.6 Ethical guidelines and best practices
7.8 Challenges and ethical dilemmas in robotics
7.8.1 Human-robot interaction challenges
7.8.2 Ethical use of artificial intelligence in robotics
7.8.3 Privacy and data security concerns
7.8.4 Employment displacement and economic impacts
7.8.5 Ethical decision-making in autonomous systems
7.9 Future directions and emerging issues in robotics
7.9.1 Advancements in artificial intelligence and machine learning
7.9.2 Human-robot collaboration and coexistence
7.9.3 Autonomous systems and mobility
7.9.4 Ethical and regulatory challenges
7.9.5 Sustainability and environmental impact
7.10 Types of robotics and their applications
7.11 Moral responsibility of robots
7.12 The interaction between robot and human
7.12.1 Technological aspects of human-robot interaction
7.12.2 Socio-psychological aspects of human-robot interaction
7.12.3 Future directions in human-robot interaction
7.13 Ethical problems of using medical robots
7.14 Criminal behavior of robots
7.15 Conclusion
References
Chapter 8: Metaverse-enhanced deep neural network approach for detecting and classifying catheters in chest X-rays
Agam Madan, Ankit Chaudhary, Vedika Gupta, and Narayan Ranjan Chakraborty
8.1 Introduction
8.2 Related work
8.3 Data
8.4 Method
8.4.1 Preprocessing
8.4.2 Transfer learning
8.4.3 EfficientNet B7
8.4.4 Training and fine-tuning
8.5 Experimental results
8.6 Evaluation
8.7 Conclusion
References
Chapter 9: Case study, future scope, and vision of metaverse in diagnosing brain tumor disease
Kamini Lamba and Shalli Rani
9.1 Introduction
9.1.1 Evolution and background of the metaverse
9.1.2 Evolution and background of brain tumor disease
9.1.3 Integration of metaverse in brain tumor diagnosis and treatment
9.1.4 Components of metaverse
9.2 Existing work
9.3 Case studies
9.4 Future scope of metaverse in diagnosing brain tumors
9.4.1 Technological advancements
9.4.2 Personalized medicine
9.4.3 Global collaboration
9.4.4 Integration with emerging technologies
9.5 Vision for the future
9.5.1 Integration of advanced technologies: comprehensive diagnostic platforms
9.5.2 Patient-centric care: personalized treatment plans
9.5.3 Enhanced medical education and training: immersive training environments
9.5.4 Robust global healthcare networks: global collaboration
9.5.5 Ethical and regulatory considerations: ensuring ethical practices
9.6 Integration of future scope and vision of metaverse technologies in diagnosing brain tumor diseases
9.6.1 Technological advancements
9.6.2 Integration with healthcare systems
9.6.3 Global accessibility and impact
References
Further reading
Chapter 10: Robotic revolution: ethical and legal frontiers in healthcare metaverse research
Azamat Ali
10.1 Introduction
10.2 The role of robotics in healthcare research
10.2.1 Robotics in diagnostics
10.2.2 Robotics in treatment
10.2.3 Robotics in surgery
10.2.4 Robotics in patient care
10.3 Ethical considerations in robotics research
10.3.1 Patient autonomy in virtual healthcare interactions
10.3.2 Privacy and data security challenges
10.3.3 Algorithmic bias and fairness
10.3.4 Implications for patient well-being and healthcare equity
10.4 Legal frameworks governing robotics in healthcare
10.4.1 Medical device regulations
10.4.2 Product liability laws
10.4.3 Data protection regulations
10.4.4 Intellectual property rights
10.4.5 Analysis of liability issues
10.4.6 Regulatory compliance challenges
10.5 Navigating ethical and legal challenges in robotics research
10.5.1 Strategies for addressing ethical dilemmas in robotics research
10.6 Recommendations for developing ethical guidelines specific to healthcare in the metaverse
10.6.1 Suggestions for navigating legal complexities and ensuring compliance with regulations
10.7 Case studies illustrating ethical and legal challenges in robotics research
10.7.1 Case study 1: Da Vinci Surgical System and patient safety
10.7.2 Case study 2: Ethical use of artificial intelligence in diagnostic robotics
10.7.3 Case study 3: Regulatory compliance and telemedicine robotics
10.7.4 Case study 4: Robotics in elderly care and ethical considerations
10.8 Conclusion and future directions
References
Chapter 11: Adaptive virtual reality exposure therapy and motor rehabilitation from Hebbian learning rule in metaverse: (Δ W/ij) for psychoanalysis
Akey Sungheetha, R. Rajesh Sharma, John Blake, and B.P. Sreeja
11.1 Introduction
11.2 Literature study analysis
11.2.1 Virtual reality in exposure therapy
11.2.2 Handling pain by virtual reality
11.2.3 Virtual reality in motor rehabilitation
11.2.4 Challenges and limitations
11.2.5 Emerging trends and future directions
11.3 Methodology
11.3.1 Adaptive virtual reality exposure therapy system
11.3.2 Flowchart of the adaptive virtual reality exposure therapy system
11.3.3 VR-based motor rehabilitation system
11.3.4 Flowchart of the virtual reality-based motor rehabilitation system
11.4 Results and discussion
11.4.1 Experimental setup
11.4.2 Performance metrics
11.4.3 Results
11.4.4 Discussion
11.5 Conclusion
References
Further reading
Chapter 12: Implementing virtual patient simulation and surgical training in medical school: finding Data-S and Learning-R
Akey Sungheetha, R. Rajesh Sharma, Oluwasegun Julius Aroba, and B.P. Sreeja
12.1 Introduction
12.2 Literature study analysis
12.2.1 Virtual patient simulations
12.2.2 Virtual surgical training
12.2.3 Immersive anatomical education
12.2.4 Challenges and limitations
12.2.5 Emerging trends and future directions
12.3 Methodology
12.3.1 Adaptive virtual patient simulation system
12.3.2 Collaborative virtual surgical training platform
12.4 Results and discussion
12.4.1 Experimental setup
12.4.2 Performance metrics
12.4.3 Discussion
12.4.4 Collaborative virtual surgical training performance
12.4.5 Engagement and knowledge retention
12.4.6 Limitations and future work
12.5 Conclusion
References
Further reading
Chapter 13: Healthcare frontiers in the metaverse: innovations and impacts
Nikita Sharma, Kriti Sankhla, Hemlata Jain, and Ajay Khunteta
13.1 Introduction
13.1.1 Summary of the metaverse
13.1.2 Healthcare’s evolution
13.1.3 The scene of the metaverse
13.2 Definition and elements
13.2.1 Definition of the metaverse
13.2.2 Core elements of the metaverse
13.2.3 New technologies: the revolution in digital health
13.3 Wearables and health monitoring
13.3.1 Virtual reality in healthcare
13.3.2 Telehealth in the metaverse
13.4 Virtual reality treatments
13.4.1 Simulated surgery
13.4.2 Medical artificial intelligence and machine learning
13.5 Predictive analytics and diagnostics in medicine
13.5.1 Data security and blockchain
13.6 Protecting patient confidentiality with open health records
13.6.1 Comprehensive medical education
13.7 Simulated training environments in virtual medical schools
13.7.1 Remote patient monitoring and telemedicine
13.8 Metaverse device connectivity
13.8.1 Real-time health monitoring
13.9 Online communities of support
13.9.1 Empowerment of patients
13.9.2 Platforms for mental health and well-being
13.10 The role of robotics
13.10.1 Robots for surgery
13.10.2 Artificial intelligence-powered assistive technology
13.11 Case study for research effective applications
13.11.1 Study 1: virtual reality-assisted surgical training
13.11.2 Study 2: virtual reality-based exposure therapy for anxiety disorders
13.11.3 Study 3: virtual reality for burn patients’ pain management
13.11.4 Study 4: virtual reality-based cognitive rehabilitation for stroke patients
13.12 Legal aspects for ethical research privacy issues
13.12.1 Fairness and availability in the metaverse’s legal environment
13.12.2 Handling legal obstacles
13.12.3 Standardization and conformance
13.13 Future scope and challenges
13.13.1 Anticipated research gaps and challenges
13.13.2 New trends and innovations
13.14 Vision of the metaverse in healthcare
References
Further reading
Chapter 14: Vision of the metaverse in healthcare
Shagun Arora and Jyoteesh Malhotra
14.1 Introduction
14.2 Materials and methods
14.2.1 Search strategy
14.2.2 Eligibility criteria
14.2.3 Data sources, study selection, and data extraction
14.3 Results
14.3.1 Characteristics of the included studies
14.4 Discussion
14.4.1 Metaverse for prevention and treatment
14.4.2 Metaverse and research application
14.4.3 Limitations of the metaverse
14.4.4 Challenges and open issues in metaverse healthcare
14.4.5 Future directions in metaverse healthcare
14.5 Conclusions
References
Chapter 15: Case study, future scope and vision of metaverse in healthcare
Priyanka Datta, Neha Garg, Amitesh Aggarwal, Amanpreet Kaur, and Yonis Gulzar
15.1 Introduction
15.1.1 History
15.1.2 Application of metaverse
15.1.3 Metaverse in healthcare
15.2 Case study
15.2.1 Case study 1: innerworld
15.2.2 Case study 2: Karuna Labs
15.2.3 Comparison of case studies
15.3 Future scope and limitations
15.4 Conclusion
References
Further reading
Chapter 16: The role of robotics: deep Q-network-based fusion for healthcare metaverse applications
Akey Sungheetha, R. Rajesh Sharma, Oluwasegun Julius Aroba, and B.P. Sreeja
16.1 Introduction
16.1.1 Key facets to recognize the functions of robotics
16.2 Literature study analysis
16.2.1 Robotic kinematics and dynamics
16.2.2 Control algorithms
16.2.3 Sensing and perception
16.2.4 Human-robot interaction
16.2.5 Challenges and future directions
16.2.6 Identified future research directions
16.3 Methodology
16.3.1 Overview of the proposed approach
16.4 Results and discussion
16.4.1 Experimental setup
16.4.2 Performance metrics
16.4.3 Discussion
16.5 Conclusion
References
Further reading
Chapter 17: Transforming healthcare in the metaverse with impact of wearable technology
Ankita Sharma and Shalli Rani
17.1 Introduction
17.2 Applications of healthcare in metaverse
17.2.1 Medical training
17.2.2 Virtual consultation
17.2.3 Remote monitoring and integration of wearables
17.2.4 Mental healthcare
17.2.5 Virtual wellness
17.3 Metaverse with the XR technology
17.3.1 Artificial intelligence for the metaverse in healthcare
17.4 Use of metaverse in healthcare
17.4.1 Use cases of metaverse in artificial intelligence
17.5 Security challenges in metaverse in healthcare
17.6 Conclusion
17.7 Future work
References
Chapter 18: Optimal diabetes prediction via synergistic hyperparameter tuning and Synthetic Minority Oversampling Technique techniques
Devanshi Gupta, Kanwarpartap Singh Gill, Rahul Chauhan, and Hemant Singh Pokhariya
18.1 Introduction
18.2 Literature
18.3 Input dataset
18.4 Proposed methodology
18.5 Results
18.5.1 Confusion matrix analysis
18.5.2 Tables and figures
18.6 Conclusion
18.7 Future scope
References
Further reading
Chapter 19: Raspberry Pi-enhanced health tracking for complete well-being solutions
Agampreet Singh, Kanwarpartap Singh Gill, Mukesh Kumar, and Ruchira Rawat
19.1 Introduction
19.2 Literature
19.3 Proposed methodology
19.4 Results
19.4.1 Improved e-health monitoring
19.4.2 Pulse acquisition module
19.4.3 Body temperature measuring component
19.4.4 Parametric readings percentage analysis
19.5 Conclusion
References
Chapter 20: Developers’ engineering of metaverse and healthcare procurements
Harbani Sharma, Sheena Angra, and Bhanu Sharma
20.1 Introduction
20.2 Related work
20.3 Immersive technologies
20.4 Metaverse-healthcare: a radical collaboration
20.5 Challenges of using metaverse in healthcare
20.6 Devices used in metaverse
20.7 Conclusion
References
Index
- Edition: 1
- Published: July 31, 2025
- Imprint: Academic Press
- Language: English
SR
Shalli Rani
Shalli Rani completed her Post-doc from Manchester Metropolitan University, UK in June , 2023. She is Professor at Chitkara University Institute of Engineering and Technology, India . She has 2 decades of teaching experience. She received Ph.D. degree in Computer Applications from Punjab Technical University, Jalandhar. Her main area of interest, Machine Learning and Internet of Things. She has published/accepted/presented more than 100+ papers in international journals /conferences (SCI+Scopus) and edited/authored several books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters.. She received a young scientist award in Feb. 2014 from Punjab Science Congress, Lifetime Achievement Award and Supervisor of the year award from Global Innovation and Excellence, 2021.
Affiliations and expertise
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India