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Metaverse technologies in healthcare /

This book explores the integration of metaverse technologies in the healthcare industry, covering various aspects such as blockchain, digital twins, IoT, and immersive technologies. It aims to demonstrate how these advancements can enhance healthcare delivery, telehealth, medical education, and pati...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Dhanaraj, Rajesh Kumar
Format: eBook
Language:English
Published: London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2024]
Subjects:
Artificial intelligence > Medical applications.
Metaverse.
Virtual reality in medicine.
Artificial intelligence.
Artificial Intelligence
Réalité virtuelle en médecine.
Intelligence artificielle.
artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Metaverse Technologies in Healthcare
  • Metaverse Technologies in Healthcare
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • 1
  • Metaverse: A vehicle in digital healthcare
  • 1. Introduction
  • 2. Significant metaverse healthcare technologies
  • 2.1 Blockchain implementation and healthcare development in the metaverse
  • 2.2 Healthcare using digital twins with metaverse
  • 2.3 Internet of Things
  • 2.4 Edge/cloud computing
  • 2.5 5G/6G network
  • 2.6 Immersive technology
  • 2.7 Human-computer interaction
  • 2.8 Quantum computing
  • 2.9 Three-dimensional reconstruction
  • 2.10 Extended reality
  • 2.11 Web3
  • 2.12 M-worlds
  • 3. Opportunities in healthcare metaverse
  • 3.1 Telehealth
  • 3.2 Training and education in medicine
  • 3.3 Healthcare metaverse surgery
  • 3.4 Better mental healthcare via the metaverse
  • 4. Metaverse's contributions to healthcare and patient care
  • 4.1 Metaverse's digital therapy applications
  • 4.2 Surgical procedures using augmented reality
  • 4.3 Metaverse in radiology
  • 4.4 Metaverse in curing chronic diseases
  • 4.4.1 Holographic architecture
  • 4.4.2 Simulation with holograms
  • 4.4.3 Combining the real with the virtual
  • 4.4.4 Linkage between the virtual and the real
  • 4.5 Metaverse and mental health
  • 5. Within the metaverse, remote assistance for critical patients
  • 5.1 Metaverse phases
  • 5.1.1 Traditional healthcare industry
  • 5.1.2 Healthcare 4.0
  • 5.1.3 First phase of Healthcare 4.0
  • 5.1.4 Second phase of Healthcare 4.0
  • 6. Primary effects of the metaverse on the healthcare industry
  • 6.1 Telemedicine 2.0: Virtual hospital
  • 6.2 Modern surgery utilizing the metaverse
  • 6.3 Metaverse-powered advanced medical education
  • 6.4 Medical education
  • 6.4.1 Osso VR
  • 6.4.2 FundamentalVR
  • 6.4.3 Specific OS
  • 7. Benefits of metaverse in healthcare.
  • 7.1 Increased access
  • 7.2 Better outcomes
  • 7.3 Lower costs
  • 8. Limitations of the metaverse in healthcare
  • 9. Integration of healthcare in the metaverse: Challenges
  • 9.1 HIPAA accordance
  • 9.2 Interoperability
  • 9.3 Rules for the metaverse
  • 10. Issues with security and privacy in the metaverse
  • 10.1 Interoperability issues
  • 10.2 Expensive platform
  • References
  • 2
  • Human computer interactive applications based on metaverse for medical ecosystem
  • 1. Introduction
  • 1.1 Metaverse-HCI in medical systems
  • 2. Importance of HCI in healthcare
  • 3. Review of present studies and research
  • 4. Challenges and limitations
  • 5. Conclusion
  • References
  • 3
  • Development of metaverse techniques during and post COVID-19 era
  • 1. Introduction
  • 1.1 Metaverse technologies
  • 1.2 Effects of the COVID-19 pandemic
  • 2. Adoption of metaverse in the COVID-19 pandemic
  • 2.1 Demand for metaverse during and post pandemic
  • 3. Review of related works
  • 4. Challenges and limitations of the survey
  • 5. Conclusion
  • References
  • 4
  • Tools and applications for telesurgery in healthcare industry
  • 1. Introduction to telesurgery
  • 2. Importance and benefits of telesurgery in healthcare
  • 3. Telecommunication infrastructure for telesurgery
  • 3.1 Surgical robots and remote manipulators
  • 4. Overview of robotic surgical systems
  • 5. Teleoperated robotic surgery
  • 6. Haptic feedback and tactile sensing in telesurgery
  • 7. Advances in surgical robot design and capabilities
  • 8. Imaging and visualization technologies
  • 9. Role of imaging in telesurgery
  • 10. 3D visualization and augmented reality
  • 11. Real-time image transmission and processing
  • 12. Integration of imaging modalities for teleoperation
  • 13. Human-machine interfaces
  • 14. Design considerations for telesurgical interfaces
  • 15. Gesture recognition and motion tracking.
  • 16. Voice control and natural language processing
  • 17. User experience and ergonomic considerations
  • 18. Telesurgery applications
  • 19. Minimally invasive procedures through telesurgery
  • 20. Remote surgery in emergency and disaster situations
  • 21. Telesurgery in rural and underserved areas
  • 22. Collaborative telesurgery and teleproctoring
  • 23. Training and education in telesurgery
  • 24. Simulation and virtual reality training for telesurgery
  • 25. Curriculum development for telesurgical education
  • 26. Certification and credentialing in telesurgery
  • 27. Continuing education and professional development
  • 28. Challenges and future directions
  • 29. Regulatory and legal considerations for telesurgery
  • 30. Ethical implications of remote surgery
  • 31. Integration of artificial intelligence in telesurgery
  • 32. Future trends and potential advancements in telesurgical tools and applications
  • 33. Virtual reality in telesurgery
  • 34. Introduction to VR technology in healthcare
  • 35. Advantages of incorporating VR into telesurgery
  • 36. Immersive visualization and presence in VR surgeries
  • 37. VR surgical planning and simulation
  • 38. Telepresence and collaboration in VR surgeries
  • 39. VR-assisted rehabilitation and patient education
  • 40. Hand gesture processing and control in telesurgery
  • 41. Importance of hand gesture control in telesurgery
  • 42. Hand gesture recognition techniques
  • 43. Integration of hand gesture control in surgical robotics
  • 44. Advantages and limitations of hand gesture control
  • 45. Latency reduction in telesurgery
  • 46. Understanding latency and its impact on telesurgical procedures
  • 47. Techniques for latency reduction
  • 48. Network optimization and QoS considerations
  • 49. Future directions for further latency reduction
  • 50. Haptic feedback in telesurgery.
  • 51. Importance of haptic feedback in surgical procedures
  • 52. Haptic feedback technologies and systems
  • 53. Challenges and solutions for haptic feedback in telesurgery
  • 54. Integration of haptic feedback in robotic surgical systems
  • 55. Integration of virtual reality, robotics, and augmented reality in telesurgery
  • 56. Synergies between VR, robotics, and AR in telesurgical applications
  • 57. Enhanced surgical visualization and navigation with AR
  • 58. Integration of robotic systems and VR/AR technologies
  • 59. Advancements in telesurgical systems enabled by VR, robotics, and AR
  • 60. Advantages and disadvantages of telesurgery
  • 61. Advantages of telesurgery in healthcare
  • 62. Limitations and challenges of telesurgery
  • 63. Ethical considerations and patient safety in telesurgery
  • 64. Conclusion
  • References
  • 5
  • Artificial intelligence-based augmented reality and virtual reality models for healthcare industry
  • 1. Introduction
  • 1.1 Augmented reality
  • 1.2 Virtual reality
  • 2. AR/VR in medical education and clinical care
  • 2.1 AR/VR in medical imaging
  • 2.2 AR/VR in telemedicine
  • 2.3 AR/VR in surgery
  • 2.4 AR/VR in anatomy learning
  • 3. AR/VR in various medical fields
  • 3.1 AR/VR in dentistry
  • 3.2 AR/VR in obstetrics
  • 3.3 AR/VR in oncology
  • 3.4 AR/VR in orthopedics
  • 4. Discussion
  • 4.1 Advantages of AR/VR in healthcare
  • 4.2 Limitations of AR/VR in healthcare
  • 5. Conclusion
  • References
  • 6
  • Blockchain strategies for medicine and health science
  • 1. Introduction
  • 1.1 Types of blockchain
  • 2. Blockchain in healthcare
  • 3. Blockchain for health record maintenance
  • 3.1 Strengths
  • 3.2 Weaknesses
  • 3.3 Opportunities
  • 3.4 Threats
  • 3.5 Importance of a system with blockchain
  • 3.6 Architecture of blockchain-based health record management systems.
  • 3.7 Blockchain supported by multilevel authentication
  • 4. Blockchain for medical supply chain management
  • 4.1 Architecture of blockchain-based medical supply chain management
  • 4.2 Supply chain events and transactions
  • 5. Blockchain for medical credentialing
  • 5.1 Strengths
  • 5.2 Weaknesses
  • 5.3 Opportunities
  • 5.4 Threats
  • 6. Blockchain in genomic market
  • 6.1 Strengths
  • 6.2 Weaknesses
  • 6.3 Opportunities
  • 6.4 Threats
  • 6.5 Architecture for genomic data sharing systems using blockchain
  • 7. Conclusion
  • References
  • 7
  • Machine learning based models for implementing digital twins in healthcare industry
  • 1. Introduction
  • 2. Digital twin
  • 2.1 What is a digital twin?
  • 2.2 History of digital twin
  • 2.3 Characteristics of digital twin
  • 2.4 Architecture of digital twin
  • 2.5 Variants of digital twin
  • 2.6 Types of digital twin
  • 2.7 Requirements of digital twin
  • 2.8 Applications of digital twin
  • 3. Artificial intelligence, machine learning, and deep learning
  • 3.1 Artificial intelligence
  • 3.2 Machine learning
  • 3.3 Deep learning
  • 3.4 Relationship between AI, ML, and DL
  • 4. The healthcare sector
  • 4.1 Healthcare sector
  • 4.2 Industries within the healthcare sector
  • 4.2.1 Drugs
  • 4.2.2 Medical equipment
  • 4.2.3 Healthcare facilities
  • 4.3 Healthcare providers and professionals
  • 4.4 Transfer of medical care
  • 5. Digital twins in healthcare industry
  • 5.1 Digital twin and health management
  • 5.2 Digital twin and treatment of diseases
  • 6. Machine learning-based models for implementing digital twins in healthcare industry
  • 6.1 ML-based digital twins for bioprinting
  • 6.2 Insightful, context-aware internet-of-things health care based on machine learning-based digital twins
  • 6.3 ML-based digital twins for brain image fusion
  • 6.4 ML-based digital twins for clinical decision support system.