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Submit a ProposalSurgical Robots in Smart Hospitals
 | Edited by Amit Kumar Tyagi, Khushboo Tripathi, Shrikant Tiwari and V. Hemamalini Copyright: 2025 | Status: Published ISBN: 9781394355327 | Hardcover | 736 pages Price: $288 USD  |
One Line Description
Transform your understanding of modern medicine with this essential book, which provides a comprehensive overview of how surgical robots are reshaping healthcare and enabling the rise of smart hospitals.
Audience
Surgeons and surgical teams, hospital administrators, healthcare executives and policymakers, engineers, and researchers involved with robotics in the medical field.
Surgeons and surgical teams, hospital administrators, healthcare executives and policymakers, engineers, and researchers involved with robotics in the medical field.
Description
The rapid evolution of technology has revolutionized the field of medicine, and nowhere is this more evident than in the integration of surgical robots into modern healthcare systems. As we stand on the brink of a new era in medical science, the concept of smart hospitals has emerged as a beacon of innovation, efficiency, and precision. This book explores the transformative role of robotic systems in surgical procedures and their integration into the broader framework of intelligent healthcare ecosystems. The journey of surgical robots began decades ago, with the first robotic-assisted surgeries marking a significant milestone in medical history. Since then, advancements in robotics, artificial intelligence, machine learning, and data analytics have propelled these systems into the forefront of modern surgery.
This book is designed to provide a comprehensive overview of the current state of surgical robotics and their role in smart hospitals. It also explores the ethical and societal implications of this technological revolution. As robots take on more significant roles in healthcare, questions about accountability, privacy, and the human touch in medicine become increasingly important.
Readers will find this volume:
• Provides a thorough introduction to surgical robots, including their history, types, benefits, and current state of adoption;
• Explores the infrastructure requirements for surgical robots, including operating room design, networking, and data management systems;
• Covers a range of clinical applications for surgical robots, including general surgery, urology, gynecology, orthopedics, and neurosurgery;
• Discusses training programs for surgical robots, including simulation-based training, mentorship, and certification for surgical robot operators;
• Evaluates the future directions for surgical robots, including the potential of artificial intelligence, augmented reality, and haptic feedback.
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The rapid evolution of technology has revolutionized the field of medicine, and nowhere is this more evident than in the integration of surgical robots into modern healthcare systems. As we stand on the brink of a new era in medical science, the concept of smart hospitals has emerged as a beacon of innovation, efficiency, and precision. This book explores the transformative role of robotic systems in surgical procedures and their integration into the broader framework of intelligent healthcare ecosystems. The journey of surgical robots began decades ago, with the first robotic-assisted surgeries marking a significant milestone in medical history. Since then, advancements in robotics, artificial intelligence, machine learning, and data analytics have propelled these systems into the forefront of modern surgery.
This book is designed to provide a comprehensive overview of the current state of surgical robotics and their role in smart hospitals. It also explores the ethical and societal implications of this technological revolution. As robots take on more significant roles in healthcare, questions about accountability, privacy, and the human touch in medicine become increasingly important.
Readers will find this volume:
• Provides a thorough introduction to surgical robots, including their history, types, benefits, and current state of adoption;
• Explores the infrastructure requirements for surgical robots, including operating room design, networking, and data management systems;
• Covers a range of clinical applications for surgical robots, including general surgery, urology, gynecology, orthopedics, and neurosurgery;
• Discusses training programs for surgical robots, including simulation-based training, mentorship, and certification for surgical robot operators;
• Evaluates the future directions for surgical robots, including the potential of artificial intelligence, augmented reality, and haptic feedback.
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Author / Editor Details
Amit Kumar Tyagi, PhD is an Assistant Professor at the National Forensic Sciences University, Gandhinagar, Gujarat, India. He has published over 350 papers in refereed international journals, conferences, authored five books and edited more than 40 books. He has also filed more than 15 patents (nationally and internationally) in the area of deep learning, Internet of Things, cyber-physical systems and computer vision.
Amit Kumar Tyagi, PhD is an Assistant Professor at the National Forensic Sciences University, Gandhinagar, Gujarat, India. He has published over 350 papers in refereed international journals, conferences, authored five books and edited more than 40 books. He has also filed more than 15 patents (nationally and internationally) in the area of deep learning, Internet of Things, cyber-physical systems and computer vision.
Khushboo Tripathi, PhD is a Professor in the Department of Computer Science and Applications, Sharda University, Greater Noida, India. Her area of interest is Wireless Ad Hoc Networks, Secure Routing Protocols, Network and Cyber Security, Artificial Intelligence.
Shrikant Tiwari, PhD is a Professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, Gautam Buddha Nagar, Uttar Pradesh, India. He has published over 85 papers in international journals and conferences, edited ten books and filed more than 10 patents, both nationally and internationally, in the areas of deep learning, the Internet of Things, cyber-physical systems and computer vision.
V. Hemamalini, PhD is an Associate Professor in the Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu Tamil Nadu, India. She has more than 29 publications in journals, conferences and books as well 4 patents.
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Table of Contents
Preface
Part 1: Fundamentals of Surgical Robots in Smart Hospitals
1. Defining Smart Hospitals
V. Karthikeyan, Y. Palinvisu and Anita Antwiwaa
1.1 Introduction and Motivation
1.2 Materials and Methods
1.2.1 Research Methodology and Context
1.2.2 Stakeholders
1.2.3 Information Collection
1.2.4 Data Analytics
1.3 Analytical Requirements for Intelligent Healthcare Facilities
1.4 Integration of IT Environments in Smart Hospitals
1.5 Improving Connectivity and Efficiency
1.6 Revolutionizing Healthcare with Cutting-Edge Technology
1.6.1 Comprehending DICOM in Intelligent Healthcare Facilities
1.6.2 Advantages of DICOM in Intelligent Healthcare Facilities
1.7 Optimizing Operations and Improving Patient Care
1.7.1 Procurement of Materials in Intelligent Healthcare Facilities
1.7.2 Quality Assurance (QA) in Material Management
1.7.3 Benefits of Integration in Intelligent Healthcare Facilities
1.8 Integration of Medical Devices in Smart Hospitals
1.9 Interoperability and Research Data Export
1.10 Conclusion
References
2. AI-Driven IoT, Federated Learning, and Surgical Robotics: Revolutionizing Smart Healthcare
Garima Jain, Ankush Jain, Amita Shukla and Prashant Kumar
2.1 Introduction
2.2 Surgical Robots in Smart Hospitals: Revolutionizing Precision and Efficiency
2.2.1 IoT-Aided Robotic Systems: Definition, Architecture, and Functionalities
2.3 Architecture of an IoT-Aided Robotic System
2.3.1 Physical Layer
2.3.2 Network Control Layer
2.3.3 Application Layer
2.3.4 Cognitive and Decision Making Abilities
2.4 Role of AI in IoT-Aided Robotics
2.4.1 Machine Learning and Data Processing
2.4.2 AI in Healthcare: Diagnosis and Treatment Planning
2.4.3 AI Driven Robotic Assistance in Healthcare
2.4.4 Predictive and Personalized Medicine
2.5 AI-Driven IoT, Federated Learning, and Surgical Robotics: Revolutionizing Smart Healthcare
2.5.1 Decentralized Medical Intelligence Using Federated Learning
2.5.2 AI-Powered Diagnosis and Treatment Optimization
2.5.3 Artificial Intelligence-Based Surgical Robotics for Precision Medicine
2.5.4 Predictive Medicine and Personalized Medicine
2.6 AI-Driven IoT, Federated Learning, and Robotics: The Current Utilization and Transformation of Smart Healthcare
2.7 AI-Driven IoT, Federated Learning, Surgical Robotics, and Block Chain for Secure Collaborative EHR Analytics
2.8 Integrating IoT, Federated Learning, Surgical Robotics and AI in Healthcare: Current Applications and Motivations
2.9 Integration of IoT-Based Robotic Healthcare Systems with Robotic Assisted Surgery
2.10 Challenges and Future Directions in Federated Learning (FL) and AI-Driven Robotic Surgery: Overcoming Barriers to Collaborative Machine Learning
2.11 Conclusion
References
3. Off-Line Robot Programming: Methods and Tools
M. Anand, T. M. Sheeba, S. Albert Antony Raj, A. S. Anshy Princella and J. S. Femilda Josephin
3.1 Introduction
3.2 Off-Line Programming Methods and Tools
3.3 Benefits of Offline Programming
3.3.1 Ease of Use
3.3.2 No Interruptions Necessary
3.3.3 Enhancing Robotic Capabilities
3.3.4 Adaptability
3.4 Applications of OLP
3.5 Impact on Small Batch Manufacturing
3.6 Tools for Offline Programmed Robots
3.6.1 Fingered Gripper
3.6.2 Vacuum Gripper
3.6.3 Weld Gun
3.6.4 Paint Gun
3.6.5 Pencil Tool
3.6.6 Machining Spindle
3.6.7 Assembly Tools
3.6.8 Extruder
3.6.9 Calibration Tool
3.6.10 Custom Tool
3.7 Common Misconceptions and Misunderstandings about OLP
3.7.1 OLP is Only for Large Manufacturers
3.7.2 OLP is Difficult to Use
3.7.3 OLP is Expensive
3.7.4 OLP Eliminates the Need for Skilled Programmers
3.8 Graphical 3D Model Simulator
3.8.1 Offline Robot Simulators
3.8.2 Robot Simulation Systems
3.8.3 OLP Software
3.9 Offline Programming of Industrial Robots
3.9.1 Programming in Teach Mode
3.9.2 Programming with Teach Pendant
3.9.3 Programming with Offline Teaching Software
3.9.4 Programming with OLP and Simulation Software
3.10 Tips for Robot Offline Programming
3.11 Conclusion
References
4. Surgical Robots in Urology and Gynecology
Neha Ramesh, Praveen Joe I. R. and Vijay John
4.1 Introduction
4.2 Historical Perspective on Robotic Surgery in Urology
4.3 The da Vinci Surgical System: Architecture and Advancements
4.4 Applications of Robotic Surgery in Urology
4.4.1 Radical Prostatectomy
4.4.2 Radical Cystectomy
4.4.3 Partial Nephrectomy
4.4.4 Other Procedures
4.5 Emerging Technologies in Robotic Urology
4.5.1 Image-Guided Surgery
4.5.2 Single-Port Systems
4.6 Robotic-Assisted Gynecological Procedures
4.6.1 Hysterectomy
4.6.2 Myomectomy
4.6.3 Sacrocolpopexy
4.6.4 Other Procedures
4.7 Efficacy and Safety of Robotic-Assisted Surgery in Gynecology
4.8 Technical Aspects and Components of Robotic Surgical Systems
4.9 Advancing Robotic Platforms
4.10 Training Programs and Implementation Strategies
4.10.1 Curriculum Development
4.10.2 Simulation Training
4.10.3 Modular Training Approach
4.10.4 Assessment and Feedback
4.10.5 Implementation Strategies
4.11 Comparative Analysis of Robotic Surgery in Urology and Gynecology
4.12 Challenges and Controversies
4.13 Future Directions
4.14 Conclusion
References
5. Urology and Gynecology: Clinical Applications and Models of Surgical Robots
Snehal P. Bhagat, Amol P. Bhagat, Nikhil Band and Milind R. Dhande
5.1 Introduction
5.2 Models of Surgical Robot in Urology and Gynecology
5.3 Python-Based Implementation of Surgical Robot Model in Urology and Gynecology
5.4 Integration of Artificial Intelligence and Machine Learning in Urology and Gynecology Robotics Surgery
5.5 Conclusion
References
6. Top Algorithms in Machine Learning for Predicting Insights in Smart Applications
Nareen Jan, Javaid A. Sheikh, Immad A. Shah and Tanuj Surve
6.1 Introduction
6.1.1 Definition and Early History
6.2 Machine Learning Algorithms
6.2.1 Supervised Learning
6.2.1.1 Characteristics of Supervised Learning
6.2.1.2 Types of Supervised Learning
6.2.2 Unsupervised Learning
6.2.2.1 Clustering
6.2.2.2 Association
6.2.2.3 Dimensionality Reduction
6.2.3 Reinforcement Learning
6.3 Applications of Machine Learning Algorithms
6.3.1 Finance
6.3.2 Marketing and Sales
6.3.3 Natural Language Processing
6.3.4 Communications
6.3.5 Agriculture
6.3.6 Cyber Security
6.3.6.1 Intrusion Detection and Response
6.3.6.2 Malware Detection
6.3.6.3 Network Security
6.3.7 Education
6.3.7.1 Personalized Learning
6.3.7.2 Student Performance Prediction
6.3.7.3 Enhanced Assessment Practices
6.3.7.4 Adaptive Learning Environments
6.3.8 Transportation and Automotives
6.3.8.1 Traffic Prediction and Management
6.3.8.2 Autonomous Vehicles
6.3.8.3 Public Transit Optimization
6.3.8.4 Predictive Maintenance
6.3.8.5 Road Marking Recognition for Intelligent Vehicles
6.3.8.6 Trip and Travel Mode Detection
6.3.8.7 Quality Control in Manufacturing
6.3.8.8 Sales Forecasting
6.3.9 Entertainment and Media
6.3.9.1 Personalized Content Recommendation
6.3.9.2 Improved Audience Analytics
6.3.9.3 Real-Time Moderation and Censorship
6.3.9.4 Enhanced Gaming Experiences
6.3.9.5 Dynamic Ad Targeting
6.3.9.6 Voice and Image Recognition in Media Accessibility
6.3.10 Energy and Utilities
Acknowledgement
References
7. Smart Hospitals in Modern Era
Roopa Devi E.M., Shanthakumari R., Jayaswati P. and Mythili M.
7.1 Introduction
7.2 The Evolution of Smart Hospital
7.3 Key Components of Smart Hospital
7.3.1 The Role of IoT Devices in Patient Monitoring
7.3.2 Digitizing Healthcare with Electronic Health Records
7.3.3 Artificial Intelligence in Enhancing Clinical Decision Making
7.3.4 Telemedicine: Expanding Access to Healthcare
7.3.5 Efficiency and Precision with Automated Medication Systems
7.4 Smart Hospital Design
7.4.1 Advanced Technologies in Smart Hospital Design
7.4.2 Patient-Centered Care in Smart Hospital Design
7.4.3 Operational Efficiency and Resource Management
7.4.4 Sensing Layers in Smart Hospital Design
7.4.4.1 Wearable Sensors
7.4.4.2 Ambient Sensors
7.4.4.3 Location Sensors
7.5 Architecture of Smart Hospitals
7.5.1 Systems of Hospital Management
7.5.2 Patient Relationship Management (PRM)
7.5.3 Medical Imaging and AI-Based Analysis
7.5.4 Laboratory Information Systems
7.5.5 Patient Portals and Self-Service Solutions
7.5.6 Pharmacy Management System
7.5.7 Financial and Insurance Management
7.6 AI Integration in Smart Hospitals
7.6.1 Enhancing Patient Care Using AI
7.6.2 AI in Diagnostics and Treatment
7.6.3 Operational Efficiency and Resource Management
7.6.4 AI-Driven Decision Support Systems
7.6.5 AI in Healthcare Administration
7.6.6 The Future of AI in Smart Hospitals
7.7 Internet of Things (IoT) in Smart Hospitals
7.7.1 IoT Devices in Healthcare
7.7.2 Benefits of IoT in Smart Hospitals
7.7.3 Challenges of IoT in Healthcare
7.7.3.1 Data Protection and Privacy
7.7.3.2 Interoperability
7.7.3.3 Data Overload
7.7.3.4 Expenses
7.7.4 The Future of IoT in Healthcare
7.7.4.1 AI Incorporation
7.7.4.2 5G Networks
7.7.4.3 Blockchain for Data Security
7.7.4.4 Many More Wearables
7.8 Applications from Intelligent Hospitals
7.8.1 Real-Time Monitoring of Patients
7.8.2 Predictive Analytics and AI for Early Detection
7.8.3 Smart Hospital Infrastructure and Workflow Optimization
7.8.4 Telemedicine and Remote Consultation
7.8.5 Patient Safety and Fall Prevention
7.8.6 Personalized Medicine and Treatment Plans
7.8.7 Robotic Assistance in Hospitals
7.8.8 Enhanced Communication and Cooperation Tools
7.9 Challenges for Smart Cities
7.9.1 Data Privacy and Cybersecurity
7.9.2 Interoperability
7.9.3 High Implementation Costs
7.9.4 Social Inclusion and Equity Features of Smart City Design
7.9.5 Scalability and Flexibility
7.9.6 Environmental and Sustainability Concerns
7.9.7 Challenges in Governance and Regulation
7.9.8 Infrastructure Maintenance and Longevity
7.9.9 Public Perception and Acceptance
7.10 Comparing Traditional Hospitals and Smart Hospitals
7.10.1 Technology Usage
7.10.2 Patient Care and Monitoring
7.10.3 Patient Experience
7.10.4 Hospital Operations
7.10.5 Safety and Security
7.10.6 Data Management and Decision Making
7.10.7 Costs and Financial Management
7.10.8 Environmental Impact
7.11 Future Scope of Smart Hospitals
7.12 Conclusion
References
8. Revolutionizing Surgery in Smart Hospital with Robotics Techniques
Sarita, Khushboo Tripathi and Kashish Kumari
8.1 Introduction to Surgical Robots
8.2 Evolution of Robotic Surgeries in Healthcare
8.3 Working of Surgical Robots
8.4 Benefits and Challenges of Surgical Robots in Smart Hospitals
8.5 AI in Surgical Robot
8.6 Future of Surgical Robots and Smart Hospitals
8.7 Disinfection Robotics
8.8 Patient Care Robotics
8.9 Supply Chain Robotics and Logistics in Healthcare
8.10 Benefits of Robotic Integration in Healthcare
8.11 Overcoming Implementation Challenges
8.12 Real-World Applications of Surgical Robots
8.13 Conclusion
8.14 Future Scope
References
Part 2: Method and Applications of Surgical Robots in Smart Hospitals
9. Applications of Fog, Edge, and Cloud-Based Deployment of AI-Infused Metaverse Integration in Smart Healthcare
Ajeet Kumar Sharma, Saurabh Singhal, Avinash Kumar Sharma and Rakesh Kumar
9.1 Introduction
9.1.1 Artificial Intelligence in Smart Healthcare
9.1.2 Metaverse in Healthcare
9.2 Computing Paradigms: Cloud, Edge, and Fog Computing
9.2.1 Synergistic Integration for the Metaverse
9.2.1.1 Need for Synergy
9.3 Cloud-Based AI Deployment in the Metaverse for Smart Healthcare
9.3.1 Applications in Smart Healthcare
9.4 Edge-Based AI Deployment in AI-Infused Metaverse Healthcare
9.4.1 Applications in Smart Healthcare
9.5 Fog-Based AI Deployment in AI-Infused Metaverse Healthcare
9.5.1 Applications in Smart Healthcare
9.6 Comparative Analysis of Cloud, Edge, and Fog Deployment in AI-Infused Metaverse Healthcare
9.7 Future Trends and Research Directions in AI-Driven Healthcare
9.7.1 Blockchain and AI for Data Security
9.7.2 6G and AI-Powered Healthcare
9.8 Conclusion
References
10. Intelligent Chatbot for Personalized Mental Healthcare Support
R. Gayathri, T. Perarasi, R. Ramkumar and M. Adbullah
10.1 Introduction
10.1.1 The Role of Chatbots in Mental Healthcare
10.1.2 Customizing and Providing Emotional Support via Chatbots
10.1.3 Technical Foundations of Mental Health Chatbots
10.1.4 Challenges and Ethical Considerations
10.2 Methodology
10.2.1 The Importance of Dataset Quality for Mental Healthcare Chatbots
10.2.2 Dataset Collection
10.2.3 Data Labeling for Sentiment and Intent Recognition
10.2.4 Intent Recognition
10.2.5 Data Preprocessing
10.2.5.1 Tokenization
10.2.5.2 Stop Word Removal
10.2.5.3 Lemmatization
10.2.5.4 Sentiment Labeling
10.2.6 Fine-Tuning of NLP Model
10.2.7 Model Selection
10.2.8 Transfer Learning
10.2.9 Sentiment Analysis
10.2.10 Intent Detection
10.2.11 Model Training and Evaluation
10.3 Continuous Improvement
10.4 Proposed Work Modules
10.4.1 Smart Interfaces
10.4.1.1 User Interface (UI)
10.4.1.2 Natural Language Processing (NLP) Model
10.4.1.3 Continuous Learning
10.5 Results and Discussion
10.5.1 Inferences
10.5.1.1 Accuracy Graph for Sentiment Analysis
10.5.1.2 Intent Recognition Results
10.5.1.3 ChatBot Response Accuracy Over Time
10.5.1.4 Comparison with Related Work
10.6 Conclusion and Future Work
10.6.1 Future Work
References
11. Medical Sensor Network, Edge Computing, and Blockchain for Efficient Medical Data Exchange
Amit Kumar Tyagi and Richa
11.1 Introduction to Medical Sensor Network, Blockchain Technology and Edge Computing: Challenges and Future Scope
11.1.1 Challenges
11.1.2 Future Scope
11.2 Background Work
11.3 Medical Sensor Networks in Healthcare: Overview, Types, Importance and Applications
11.4 Edge Computing in Healthcare: Definition, Concepts, Advantages and Challenges and Opportunities
11.5 Blockchain Technology in Healthcare: Principles, Use Cases and Benefits and Challenges
11.5.1 Popular Issues of Security and Privacy in Healthcare
11.6 Blockchain, Medical Sensor Networks, and Edge Computing Integration for a Safer, Secure Healthcare Services
11.6.1 Challenges in Integration of Blockchain, Medical Sensor Networks, and Edge Computing
11.6.2 Benefits and Impacts of Blockchain, Medical Sensor Networks, and Edge Computing Integration
11.7 Use Cases and Applications of Emerging Technology in Medical Data Exchanges
11.8 Issue and Challenges Toward Using Technologies in Healthcare Sector
11.8.1 Legal, Regulatory, Security, and Privacy Issue in Medical Data Exchange
11.8.2 Patient Consent and Control Required in Medical Sector (Using Emerging Technology)
11.8.3 Technical and Nontechnical Challenges Toward Using Emerging Technologies in Medical Data Exchanges
11.9 Future Research Opportunities Toward Using Edge Computing, Blockchain, and Medical Sensor Networks in Healthcare Sector
11.10 Conclusion
References
12. Impact of AI on Robotics Surgery: A Comprehensive Overview
M. Ramprasath, Elangovan G., Prakash Duraisamy and V. Kavitha
12.1 Introduction
12.1.1 Ethical Challenges in Robotic Surgery
12.1.2 Investigative Inquiry in Robotic Surgery
12.2 Related Works
12.2.1 Investigation about Robotic Surgery
12.2.2 Different Level in Robotic Technologies
12.2.3 Advancements in AI-Enhanced Operational Robotics Technologies
12.3 Exploring the Moral and Legal Environment of RAS
12.4 Computer Vision and Neurological Surgery
12.4.1 Role of AI and ML in Robotic Technologies
12.4.2 Research Area in Robotic System
12.5 Neural Surgery and Robotics
12.5.1 Classification of Surgical Robotic Systems
12.5.2 Neurosurgical Robots
12.6 Conclusion
References
13. Enhancing Healthcare and Monitoring Systems with Wearable Devices
Shrikant Tiwari, Kanchan Naithani and Ramesh Wadawadagi
13.1 Introduction
13.1.1 Overview of the Current Market and Trends in Wearable Technology
13.2 Technology Behind Wearable Devices
13.2.1 Types of Sensors Used in Wearable Devices
13.2.2 Data Acquisition and Processing
13.3 Wireless Connectivity and Integration with Other Devices
13.3.1 Benefits Wearable Technology
13.4 Applications of Wearable Devices in Healthcare
13.4.1 Fitness Tracking and Activity Monitoring
13.4.2 Monitoring of Vital Signs
13.4.3 Sleep Tracking and Analysis
13.4.4 Chronic Disease Management
13.5 Design and Development of Wearable Devices
13.5.1 User-Centered Design Principles
13.5.2 Challenges in Developing Wearable Devices for Healthcare
13.5.3 Regulatory Considerations for Medical Devices
13.6 Data Analysis and Interpretation
13.6.1 Machine Learning Algorithms for Predictive Analysis
13.7 Ethical and Legal Considerations
13.7.1 Liability and Responsibility of Device Manufacturers and Healthcare Providers
13.7.2 Legal Frameworks and Regulations for Wearable Devices in Healthcare
13.8 Future Directions and Opportunities
13.8.1 Potential Applications of Wearable Devices in Healthcare
13.8.2 Challenges and Future Directions for Research and Development
13.9 Conclusion
References
14. Surgical Robots in Smart Hospitals: Enhancing Precision and Patient Outcomes
Arun Kumar Singh, Juhi Singh, Shishir Singh Chauhan and Ankit Chauhan
14.1 Introduction
14.1.1 Surgical Robotics and Sustainable Healthcare
14.1.2 Technological Advancements and System Capabilities
14.1.3 Smart Hospitals: The Digital Transformation of Healthcare
14.1.4 Convergence of Surgical Robotics and Smart Hospital Technologies
14.2 Background and Literature Review
14.2.1 Recent Research Trends and Clinical Outcomes
14.2.2 Case Studies
14.2.2.1 Integration of Surgical Robots with Smart Hospital Systems
14.2.2.2 Emerging Applications in Specialized Surgeries
14.2.2.3 Data-Driven Outcome Improvement
14.2.2.4 Patient-Centric Outcomes
14.2.3 Evaluation of Surgical Robotic Systems
14.3 Smart Hospitals: The Ecosystem of the Future
14.3.1 The Role of Data and Connectivity
14.3.2 Technological Underpinnings of Surgical Robotics
14.3.3 Advanced Imaging and Sensor Integration
14.3.4 Software, AI, and Machine Learning
14.3.5 Networked Systems and Interoperability
14.3.6 Cybersecurity and Data Integrity
14.3.7 Training and Collaborative Environments
14.3.8 Enhancing Precision through Robotics
14.4 Proposed Methodology for Integration
14.4.1 System Architecture
14.5 Advancing Sustainable Development Goals
14.6 Conclusion and Statistical Analysis
14.7 Future Research Directions
References
15. Integrating Blockchain and Telerobotic in Orthopedics: Advancing Secure and Remote Surgical Innovations
A. Ritu and A. Eshaan
15.1 Introduction
15.1.1 Various Challenges in Blockchain Designed for Robotics
15.2 Related Work
15.3 Proposed Methodology
15.3.1 VeChain (Supply Chain Blockchain for Medical Devices)
15.3.2 Algorand (Scalable Blockchain for AI and Healthcare)
15.4 Conclusion
References
16. Improving Lung Cancer Detection Using Hybrid Features and Optimized through VGG-16
A. Ritu and A. Eshaan
16.1 Introduction
16.1.1 Problem Statement and Objectives
16.2 Literature Review
16.3 Material and Methods Used
16.3.1 VGG Net 16 Architecture
16.3.1.1 Dense_Net
16.3.1.2 Transition Layers
16.3.1.3 Bottleneck Layers
16.3.2 Evaluation Metrics
16.4 Results and Discussion
16.5 Conclusion and Future Scope
References
Part 3: Issues and Challenges Towards Implementing Surgical Robots in Smart Hospitals
17. Education and Certification for Surgical Robot Operators
V. Shyamala Susan, K. Chitra Chellam, K.S. Anushya and Ir. Bambang Sugiyono Agus Purwono
17.1 Introduction
17.2 Literature Survey
17.3 Pathways Leading to Becoming a Surgical Robot Operator
17.3.1 Prerequisite for Education
17.3.2 Academic and Institutional Training Programs
17.4 Hospital Privileging and Credentialing
17.4.1 The Significance of Credentials and Privileges Gained from a Hospital
17.4.2 Basic Aspects Relating to the Credentialing and Privileging Process Undertaken by Hospitals
17.5 Recredentialing and Ongoing Competency Evaluation
17.5.1 Issues with Robotics Surgical Credentialing
17.6 Certification Requirements for Surgical Robot Operators
17.6.1 International Certification Bodies
17.6.2 Primary International Certification Authorities
17.6.3 Certification Process
17.7 Continuing Medical Education (CME) and Skill Maintenance
17.7.1 Advanced Training Courses on New Robotic Platforms
17.7.1.1 The Perks of Further Education
17.7.1.2 Types of Advanced Training Programs
17.7.1.3 Consequences of Supplementary Education on the Outcomes of Surgery
17.7.2 Participation in Conferences on Robotic Surgery
17.7.2.1 The Importance of Conferences Somewhere in the Interconnected World
17.7.2.2 Lead in Robotic Surgery Conferences
17.7.2.3 Advantages of Participation and Conferences
17.7.3 Taking Part in Research and Clinical Studies
17.7.3.1 The Significance of the Research in the Field of Robotic Surgery
17.7.3.2 Various Forms of Research Involvement
17.7.3.3 Outcomes of Research Participation
17.7.4 Regular Skills Reassessment via Simulation Modules
17.7.4.1 Why Skills Reassessment is Important
17.7.4.2 Types of Simulation Training
17.7.4.3 Impact of Regular Reassessment
17.8 Robotic Surgery Training Issues and Evolving Directions
17.8.1 Challenges in Training and Certification
17.8.1.1 High Cost of Training
17.8.1.2 Limited Access to Training Centers
17.8.1.3 Differences in Certification Requirements
17.8.1.4 Evolution of Technologies that Must Be Continuously Mastered
17.8.2 Future Trends in Robotic Surgery Education
17.8.2.1 AI-Assisted Training
17.8.2.2 Virtual and Remote Reality Training
17.8.2.3 Standardized Global Certification Programs
17.8.2.4 Multidisciplinary Training Expansion
17.9 Ethical Considerations in Robotic Surgery Training
17.9.1 Patient Safety
17.9.2 Informed Consent
17.9.3 Justifiable Distribution of Training Opportunities
17.9.4 Adherence to Policies
17.10 Conclusion
References
18. Enhancing Healthcare Cybersecurity with AI-Driven Threat Intelligence: Proactive Defense against Evolving Cyber Threats
Emmanuel Innocent Umoh, Hussaini Bishara and Avinash Kumar Sharma
18.1 Introduction
18.1.1 Major Elements Involved with AI-Based Threat Intelligence Integration in Healthcare Cybersecurity
18.2 The Evolving Healthcare Threat Landscape
18.2.1 Persistent Healthcare Cybersecurity Risks
18.2.2 Increased Use of AI-Driven Cyberattacks in Healthcare
18.2.3 New Cybersecurity Threats and their Impact on Patient Safety
18.2.4 The Role of AI in Identifying and Mitigating Cyber Threats
18.3 AI-Driven Threat Intelligence Framework for Healthcare
18.3.1 Comprehending the Architecture of AI-Driven Cybersecurity in Healthcare
18.3.1.1 Data Collection Layer
18.3.1.2 Threat Intelligence Engine
18.3.1.3 Automated Incident Response System
18.3.1.4 Federated Learning: Privacy-Preserving Security Intelligence
18.3.2 Machine Learning Techniques for Threat Detection
18.3.2.1 The Use of Machine Learning in Healthcare Cybersecurity
18.3.2.2 Machine Learning Techniques for Threat Detection in Healthcare
18.3.3 Applications of Machine Learning in Healthcare Cybersecurity
18.3.3.1 Fraud Detection in Medical Billing
18.3.3.2 Securing IoMT Devices
18.3.3.3 Identity and Access Management Improvement
18.4 AI-Driven Cybersecurity in Healthcare: Strategies, Implementation, and Case Studies
18.4.1 Integration with Healthcare IT Infrastructure
18.4.1.1 AI in Zero Trust Security Architecture
18.4.1.2 Threat Hunting Based on AI
18.4.2 Challenges in AI Implementation
18.4.3 Real-World Applications of AI in Healthcare Cybersecurity
18.4.3.1 Case Study 1: How AI Stopped a Ransomware Attack in a Hospital Network
18.4.3.2 Case Study 2: Anomaly Detection Using Artificial Intelligence in Medical Device Security
18.4.3.3 Case Study 3: Predictive Analytics for Early Threat Detection in Healthcare Cloud Services
18.4.4 Performance Indicators for AI-Driven Cybersecurity
18.4.4.1 Detection Accuracy, Precision-Recall Tradeoffs, and Response Time
18.4.4.2 Reduction in False Positives Compared to Traditional Methods
18.4.4.3 AI’s Impact on Threat Mitigation Time and Resource Efficiency
18.5 Future Directions and Proposed Model to Enhance Healthcare Cybersecurity
18.5.1 The Future of AI-Powered Security in Indian Healthcare
18.5.1.1 AI-Powered Security Collaboration for Healthcare
18.5.1.2 Blockchain-AI Synergy for Cybersecurity
18.5.1.3 Explainable AI (XAI) for Transparent Cybersecurity Decisions
18.5.2 Proposed Model: Adaptive AI-Blockchain Threat Defense System (ABTDS)
18.5.3 Policy and Regulatory Considerations
18.6 Conclusion
References
19. Advancements and Challenges in IoT Adoption for Healthcare: A Review
Ashok Kumar Jayaraman, Gayathri Ananthakrishnan, Anugna Yakkala, Pavan Tej P. G. and Tina Esther Trueman
19.1 Introduction
19.2 IoT Communication Models
19.2.1 Device to Device
19.2.2 Device to Cloud
19.2.3 Device to Gateway
19.2.4 Multimodal
19.3 Issues and Challenges in Medical Domain
19.3.1 Security and Privacy
19.3.2 Interoperability and Standards
19.3.3 Legal, Regulatory, and Rights
19.4 Artificial Intelligence of Things (AIoT)
19.5 Conclusion
References
Part 4: Near Future Development Towards Using Surgical Robots in Smart Hospitals
20. Intelligent Medical Business Assistant: Transforming Hospital Efficiency Through Advanced Analytics
Ahmed A. Esmail, Sayed M. Abdallah, Mohamed A. Elsayed, Ahmed E. Mohamed, Mostafa H. Fawzy, Ahmed Ismail Ebada and Aya M. Al-Zoghby
20.1 Introduction
20.2 Related Work
20.2.1 Existing Systems
20.2.2 Overall Problems of Existing Systems
20.2.3 Comparison Between Existing and Proposed Method
20.3 Methodology
20.4 Results and Dataset
20.5 Conclusion
References
21. Next-Generation Industrial Robots: Design and Development
Yash Suhas Jawale, Praveen Joe I. R. and Vijay John
21.1 Introduction
21.1.1 Present Conditions
21.2 Future Requirements
21.2.1 New Materials
21.2.2 Newer and Faster Processing Units
21.2.3 Customizable Hardware Via FPGA Implementation
21.3 Conclusion
References
22. A Deep Learning–Based Expression Recognition Analysis Assistant for Psychologists
Harjyot Singh Bagga, Sanjoi Sethi, Rishab Goswami, Annapurna Jonnalagadda and Ushus E.Z.
22.1 Introduction
22.2 Background
22.2.1 Research Description and Goals
22.3 Technical Specification
22.3.1 Text
22.3.2 Audio
22.3.3 Video
22.4 Design Approach and Details
22.4.1 Design Approach/Materials and Methods
22.4.2 Preparing the Data
22.4.2.1 Text
22.4.2.2 Audio
22.4.2.3 Video
22.4.3 Constraints and Trade Offs
22.4.4 Hardware and Software Requirements
22.5 Result Screenshots
22.5.1 Text
22.5.2 Audio
22.5.3 Video
22.5.4 Website Screenshots
22.5.5 Results and Discussion
22.6 Conclusions
References
23. Future Directions for Surgical Robots in Smart Hospitals
A. Sivasangari, V.J.K. Kishore Sonti, J. Cruz Antony, E. Murali and D. Deepa
23.1 Introduction
23.2 Increased Influence of Robotics in Healthcare
23.3 AI’s Role in Evolution of Modern Healthcare Industry
23.4 Integration of Robotics and AI in the Development of Advanced Surgical Robots
23.5 Smart Hospitals: From Ease of Registration to Remote Surgery and Teleoperated Haptic Technology
23.6 Futuristic Implications of Surgical Robots
23.7 Conclusion
References
24. The Prospective of Artificial Intelligence-Integrated Robotics Towards Orthopedics and Neurosurgery
Chandana Mohanty, Priya Das, Swati Swayamsiddha and Sarita Nanda
24.1 Introduction
24.1.1 Importance of AI-Robotics Toward Orthopedics
24.1.2 Importance of AI-Robotics Toward Neurosurgery
24.1.3 Organization of the Chapter
24.2 AI Robotics
24.2.1 Artificial Intelligence
24.2.1.1 Machine Learning
24.2.1.2 Neural Network
24.2.1.3 Deep Learning
24.2.1.4 Natural Language Processing
24.2.2 Robotics
24.2.2.1 Design
24.2.2.2 Action
24.2.2.3 Perception
24.2.3 AI for Human–Robot Interaction
24.3 Applications of AI-Robotics in Orthopedics Field
24.3.1 Humanoid/Robot-Assisted Orthopedics Surgery
24.3.2 Autonomous Vehicles for the Movement of Patient
24.3.3 Replacement of Caregivers and Nurses
24.3.4 Virtual Physical Therapy and Telerehabilitation
24.3.5 Radiography Analysis
24.3.6 Chatbots
24.3.7 Nanobots
24.4 Applications of AI Robotics in Neurosurgery Field
24.4.1 AI Robotics in Cranial Neurosurgery
24.4.1.1 Tumor Resection
24.4.1.2 Deep Brain Stimulation (DBS)
24.4.1.3 Intracranial Hemorrhage Management
24.4.2 AI Robotics in Spinal Neurosurgery
24.4.2.1 Spinal Fusion Surgery
24.4.2.2 Minimally Invasive Spine Surgery
24.4.2.3 Spinal Cord Injury Rehabilitation
24.4.3 AI-Robotics in Pediatric Neurosurgery
24.4.3.1 Congenital Malformation Corrections
24.4.3.2 Epilepsy Surgery
24.4.4 AI Robotics in Neurovascular Surgery
24.4.4.1 Aneurysm Clipping and Coiling
24.4.4.2 Stroke Management
24.4.5 AI Robotics in Neurorehabilitation
24.4.5.1 Robotic Exoskeletons
24.4.5.2 AI-Driven Neurofeedback Systems
24.4.6 AI-Robotics in Neurosurgical Training
24.4.6.1 Virtual and Augmented Reality Simulations
24.4.6.2 AI-Assisted Skill Assessment
24.5 Potential Impact of AI Robotics and Future Implications
24.6 Challenges and Further Research Directions
24.7 Conclusion
References
Appendix A
25. Innovative Approaches in Smart Healthcare: Artificial Intelligence Techniques Via Federated Learning
Shrikant Tiwari, Kanchan Naithani and Ramesh S. Wadawadagi
25.1 Introduction
25.1.1 Overview of Smart Healthcare Systems
25.1.1.1 IoT in Healthcare
25.1.1.2 Artificial Intelligence in Healthcare
25.1.1.3 Wearable Devices and Mobile Applications
25.1.1.4 Telemedicine and Remote Consultations
25.1.1.5 Electronic Health Records (EHR)
25.1.2 Key Challenges in Healthcare Data Management
25.2 Foundations of Artificial Intelligence in Healthcare
25.2.1 Current Trends and Developments
25.3 Federated Learning in Healthcare
25.3.1 Understanding Federated Learning
25.3.2 Advantages and Challenges in Healthcare Context
25.3.3 Advantages of Federated Learning in Healthcare
25.3.4 Challenges in Healthcare Federated Learning
25.3.5 Privacy Concerns in Healthcare Data
25.3.6 Techniques for Ensuring Patient Privacy in Federated Learning
25.4 Decentralized Model Training
25.4.1 Collaborative Model Training in Healthcare
25.4.2 Benefits of Decentralized Approaches
25.4.3 Innovative AI Techniques for Healthcare
25.4.4 Integration of AI Algorithms in Smart Healthcare Systems
25.5 Case Studies
25.5.1 Predictive Maintenance for Medical Devices
25.5.2 Disease Prediction Using Decentralized Data
25.5.3 Personalized Treatment Plans in Oncology
25.5.4 Impact and Outcomes of AI Techniques in Smart Healthcare
25.5.5 Security Measures in Federated Learning
25.5.5.1 Ensuring Data Security in Distributed Environments
25.5.5.2 Cybersecurity Measures for Healthcare Systems
25.6 Interoperability and Standards
25.6.1 Achieving Interoperability in Smart Healthcare
25.6.2 Standardization Efforts for Federated Learning in Healthcare
25.6.3 Regulatory and Ethical Considerations
25.6.3.1 Ethical Implications of AI and Federated Learning in Healthcare
25.7 Future Directions and Emerging Technologies
25.7.1 Prospects of AI and Federated Learning in Healthcare
25.7.2 Emerging Technologies Shaping the Future of Smart Healthcare
25.8 Conclusion
References
Index
Back to Top
Preface
Part 1: Fundamentals of Surgical Robots in Smart Hospitals
1. Defining Smart Hospitals
V. Karthikeyan, Y. Palinvisu and Anita Antwiwaa
1.1 Introduction and Motivation
1.2 Materials and Methods
1.2.1 Research Methodology and Context
1.2.2 Stakeholders
1.2.3 Information Collection
1.2.4 Data Analytics
1.3 Analytical Requirements for Intelligent Healthcare Facilities
1.4 Integration of IT Environments in Smart Hospitals
1.5 Improving Connectivity and Efficiency
1.6 Revolutionizing Healthcare with Cutting-Edge Technology
1.6.1 Comprehending DICOM in Intelligent Healthcare Facilities
1.6.2 Advantages of DICOM in Intelligent Healthcare Facilities
1.7 Optimizing Operations and Improving Patient Care
1.7.1 Procurement of Materials in Intelligent Healthcare Facilities
1.7.2 Quality Assurance (QA) in Material Management
1.7.3 Benefits of Integration in Intelligent Healthcare Facilities
1.8 Integration of Medical Devices in Smart Hospitals
1.9 Interoperability and Research Data Export
1.10 Conclusion
References
2. AI-Driven IoT, Federated Learning, and Surgical Robotics: Revolutionizing Smart Healthcare
Garima Jain, Ankush Jain, Amita Shukla and Prashant Kumar
2.1 Introduction
2.2 Surgical Robots in Smart Hospitals: Revolutionizing Precision and Efficiency
2.2.1 IoT-Aided Robotic Systems: Definition, Architecture, and Functionalities
2.3 Architecture of an IoT-Aided Robotic System
2.3.1 Physical Layer
2.3.2 Network Control Layer
2.3.3 Application Layer
2.3.4 Cognitive and Decision Making Abilities
2.4 Role of AI in IoT-Aided Robotics
2.4.1 Machine Learning and Data Processing
2.4.2 AI in Healthcare: Diagnosis and Treatment Planning
2.4.3 AI Driven Robotic Assistance in Healthcare
2.4.4 Predictive and Personalized Medicine
2.5 AI-Driven IoT, Federated Learning, and Surgical Robotics: Revolutionizing Smart Healthcare
2.5.1 Decentralized Medical Intelligence Using Federated Learning
2.5.2 AI-Powered Diagnosis and Treatment Optimization
2.5.3 Artificial Intelligence-Based Surgical Robotics for Precision Medicine
2.5.4 Predictive Medicine and Personalized Medicine
2.6 AI-Driven IoT, Federated Learning, and Robotics: The Current Utilization and Transformation of Smart Healthcare
2.7 AI-Driven IoT, Federated Learning, Surgical Robotics, and Block Chain for Secure Collaborative EHR Analytics
2.8 Integrating IoT, Federated Learning, Surgical Robotics and AI in Healthcare: Current Applications and Motivations
2.9 Integration of IoT-Based Robotic Healthcare Systems with Robotic Assisted Surgery
2.10 Challenges and Future Directions in Federated Learning (FL) and AI-Driven Robotic Surgery: Overcoming Barriers to Collaborative Machine Learning
2.11 Conclusion
References
3. Off-Line Robot Programming: Methods and Tools
M. Anand, T. M. Sheeba, S. Albert Antony Raj, A. S. Anshy Princella and J. S. Femilda Josephin
3.1 Introduction
3.2 Off-Line Programming Methods and Tools
3.3 Benefits of Offline Programming
3.3.1 Ease of Use
3.3.2 No Interruptions Necessary
3.3.3 Enhancing Robotic Capabilities
3.3.4 Adaptability
3.4 Applications of OLP
3.5 Impact on Small Batch Manufacturing
3.6 Tools for Offline Programmed Robots
3.6.1 Fingered Gripper
3.6.2 Vacuum Gripper
3.6.3 Weld Gun
3.6.4 Paint Gun
3.6.5 Pencil Tool
3.6.6 Machining Spindle
3.6.7 Assembly Tools
3.6.8 Extruder
3.6.9 Calibration Tool
3.6.10 Custom Tool
3.7 Common Misconceptions and Misunderstandings about OLP
3.7.1 OLP is Only for Large Manufacturers
3.7.2 OLP is Difficult to Use
3.7.3 OLP is Expensive
3.7.4 OLP Eliminates the Need for Skilled Programmers
3.8 Graphical 3D Model Simulator
3.8.1 Offline Robot Simulators
3.8.2 Robot Simulation Systems
3.8.3 OLP Software
3.9 Offline Programming of Industrial Robots
3.9.1 Programming in Teach Mode
3.9.2 Programming with Teach Pendant
3.9.3 Programming with Offline Teaching Software
3.9.4 Programming with OLP and Simulation Software
3.10 Tips for Robot Offline Programming
3.11 Conclusion
References
4. Surgical Robots in Urology and Gynecology
Neha Ramesh, Praveen Joe I. R. and Vijay John
4.1 Introduction
4.2 Historical Perspective on Robotic Surgery in Urology
4.3 The da Vinci Surgical System: Architecture and Advancements
4.4 Applications of Robotic Surgery in Urology
4.4.1 Radical Prostatectomy
4.4.2 Radical Cystectomy
4.4.3 Partial Nephrectomy
4.4.4 Other Procedures
4.5 Emerging Technologies in Robotic Urology
4.5.1 Image-Guided Surgery
4.5.2 Single-Port Systems
4.6 Robotic-Assisted Gynecological Procedures
4.6.1 Hysterectomy
4.6.2 Myomectomy
4.6.3 Sacrocolpopexy
4.6.4 Other Procedures
4.7 Efficacy and Safety of Robotic-Assisted Surgery in Gynecology
4.8 Technical Aspects and Components of Robotic Surgical Systems
4.9 Advancing Robotic Platforms
4.10 Training Programs and Implementation Strategies
4.10.1 Curriculum Development
4.10.2 Simulation Training
4.10.3 Modular Training Approach
4.10.4 Assessment and Feedback
4.10.5 Implementation Strategies
4.11 Comparative Analysis of Robotic Surgery in Urology and Gynecology
4.12 Challenges and Controversies
4.13 Future Directions
4.14 Conclusion
References
5. Urology and Gynecology: Clinical Applications and Models of Surgical Robots
Snehal P. Bhagat, Amol P. Bhagat, Nikhil Band and Milind R. Dhande
5.1 Introduction
5.2 Models of Surgical Robot in Urology and Gynecology
5.3 Python-Based Implementation of Surgical Robot Model in Urology and Gynecology
5.4 Integration of Artificial Intelligence and Machine Learning in Urology and Gynecology Robotics Surgery
5.5 Conclusion
References
6. Top Algorithms in Machine Learning for Predicting Insights in Smart Applications
Nareen Jan, Javaid A. Sheikh, Immad A. Shah and Tanuj Surve
6.1 Introduction
6.1.1 Definition and Early History
6.2 Machine Learning Algorithms
6.2.1 Supervised Learning
6.2.1.1 Characteristics of Supervised Learning
6.2.1.2 Types of Supervised Learning
6.2.2 Unsupervised Learning
6.2.2.1 Clustering
6.2.2.2 Association
6.2.2.3 Dimensionality Reduction
6.2.3 Reinforcement Learning
6.3 Applications of Machine Learning Algorithms
6.3.1 Finance
6.3.2 Marketing and Sales
6.3.3 Natural Language Processing
6.3.4 Communications
6.3.5 Agriculture
6.3.6 Cyber Security
6.3.6.1 Intrusion Detection and Response
6.3.6.2 Malware Detection
6.3.6.3 Network Security
6.3.7 Education
6.3.7.1 Personalized Learning
6.3.7.2 Student Performance Prediction
6.3.7.3 Enhanced Assessment Practices
6.3.7.4 Adaptive Learning Environments
6.3.8 Transportation and Automotives
6.3.8.1 Traffic Prediction and Management
6.3.8.2 Autonomous Vehicles
6.3.8.3 Public Transit Optimization
6.3.8.4 Predictive Maintenance
6.3.8.5 Road Marking Recognition for Intelligent Vehicles
6.3.8.6 Trip and Travel Mode Detection
6.3.8.7 Quality Control in Manufacturing
6.3.8.8 Sales Forecasting
6.3.9 Entertainment and Media
6.3.9.1 Personalized Content Recommendation
6.3.9.2 Improved Audience Analytics
6.3.9.3 Real-Time Moderation and Censorship
6.3.9.4 Enhanced Gaming Experiences
6.3.9.5 Dynamic Ad Targeting
6.3.9.6 Voice and Image Recognition in Media Accessibility
6.3.10 Energy and Utilities
Acknowledgement
References
7. Smart Hospitals in Modern Era
Roopa Devi E.M., Shanthakumari R., Jayaswati P. and Mythili M.
7.1 Introduction
7.2 The Evolution of Smart Hospital
7.3 Key Components of Smart Hospital
7.3.1 The Role of IoT Devices in Patient Monitoring
7.3.2 Digitizing Healthcare with Electronic Health Records
7.3.3 Artificial Intelligence in Enhancing Clinical Decision Making
7.3.4 Telemedicine: Expanding Access to Healthcare
7.3.5 Efficiency and Precision with Automated Medication Systems
7.4 Smart Hospital Design
7.4.1 Advanced Technologies in Smart Hospital Design
7.4.2 Patient-Centered Care in Smart Hospital Design
7.4.3 Operational Efficiency and Resource Management
7.4.4 Sensing Layers in Smart Hospital Design
7.4.4.1 Wearable Sensors
7.4.4.2 Ambient Sensors
7.4.4.3 Location Sensors
7.5 Architecture of Smart Hospitals
7.5.1 Systems of Hospital Management
7.5.2 Patient Relationship Management (PRM)
7.5.3 Medical Imaging and AI-Based Analysis
7.5.4 Laboratory Information Systems
7.5.5 Patient Portals and Self-Service Solutions
7.5.6 Pharmacy Management System
7.5.7 Financial and Insurance Management
7.6 AI Integration in Smart Hospitals
7.6.1 Enhancing Patient Care Using AI
7.6.2 AI in Diagnostics and Treatment
7.6.3 Operational Efficiency and Resource Management
7.6.4 AI-Driven Decision Support Systems
7.6.5 AI in Healthcare Administration
7.6.6 The Future of AI in Smart Hospitals
7.7 Internet of Things (IoT) in Smart Hospitals
7.7.1 IoT Devices in Healthcare
7.7.2 Benefits of IoT in Smart Hospitals
7.7.3 Challenges of IoT in Healthcare
7.7.3.1 Data Protection and Privacy
7.7.3.2 Interoperability
7.7.3.3 Data Overload
7.7.3.4 Expenses
7.7.4 The Future of IoT in Healthcare
7.7.4.1 AI Incorporation
7.7.4.2 5G Networks
7.7.4.3 Blockchain for Data Security
7.7.4.4 Many More Wearables
7.8 Applications from Intelligent Hospitals
7.8.1 Real-Time Monitoring of Patients
7.8.2 Predictive Analytics and AI for Early Detection
7.8.3 Smart Hospital Infrastructure and Workflow Optimization
7.8.4 Telemedicine and Remote Consultation
7.8.5 Patient Safety and Fall Prevention
7.8.6 Personalized Medicine and Treatment Plans
7.8.7 Robotic Assistance in Hospitals
7.8.8 Enhanced Communication and Cooperation Tools
7.9 Challenges for Smart Cities
7.9.1 Data Privacy and Cybersecurity
7.9.2 Interoperability
7.9.3 High Implementation Costs
7.9.4 Social Inclusion and Equity Features of Smart City Design
7.9.5 Scalability and Flexibility
7.9.6 Environmental and Sustainability Concerns
7.9.7 Challenges in Governance and Regulation
7.9.8 Infrastructure Maintenance and Longevity
7.9.9 Public Perception and Acceptance
7.10 Comparing Traditional Hospitals and Smart Hospitals
7.10.1 Technology Usage
7.10.2 Patient Care and Monitoring
7.10.3 Patient Experience
7.10.4 Hospital Operations
7.10.5 Safety and Security
7.10.6 Data Management and Decision Making
7.10.7 Costs and Financial Management
7.10.8 Environmental Impact
7.11 Future Scope of Smart Hospitals
7.12 Conclusion
References
8. Revolutionizing Surgery in Smart Hospital with Robotics Techniques
Sarita, Khushboo Tripathi and Kashish Kumari
8.1 Introduction to Surgical Robots
8.2 Evolution of Robotic Surgeries in Healthcare
8.3 Working of Surgical Robots
8.4 Benefits and Challenges of Surgical Robots in Smart Hospitals
8.5 AI in Surgical Robot
8.6 Future of Surgical Robots and Smart Hospitals
8.7 Disinfection Robotics
8.8 Patient Care Robotics
8.9 Supply Chain Robotics and Logistics in Healthcare
8.10 Benefits of Robotic Integration in Healthcare
8.11 Overcoming Implementation Challenges
8.12 Real-World Applications of Surgical Robots
8.13 Conclusion
8.14 Future Scope
References
Part 2: Method and Applications of Surgical Robots in Smart Hospitals
9. Applications of Fog, Edge, and Cloud-Based Deployment of AI-Infused Metaverse Integration in Smart Healthcare
Ajeet Kumar Sharma, Saurabh Singhal, Avinash Kumar Sharma and Rakesh Kumar
9.1 Introduction
9.1.1 Artificial Intelligence in Smart Healthcare
9.1.2 Metaverse in Healthcare
9.2 Computing Paradigms: Cloud, Edge, and Fog Computing
9.2.1 Synergistic Integration for the Metaverse
9.2.1.1 Need for Synergy
9.3 Cloud-Based AI Deployment in the Metaverse for Smart Healthcare
9.3.1 Applications in Smart Healthcare
9.4 Edge-Based AI Deployment in AI-Infused Metaverse Healthcare
9.4.1 Applications in Smart Healthcare
9.5 Fog-Based AI Deployment in AI-Infused Metaverse Healthcare
9.5.1 Applications in Smart Healthcare
9.6 Comparative Analysis of Cloud, Edge, and Fog Deployment in AI-Infused Metaverse Healthcare
9.7 Future Trends and Research Directions in AI-Driven Healthcare
9.7.1 Blockchain and AI for Data Security
9.7.2 6G and AI-Powered Healthcare
9.8 Conclusion
References
10. Intelligent Chatbot for Personalized Mental Healthcare Support
R. Gayathri, T. Perarasi, R. Ramkumar and M. Adbullah
10.1 Introduction
10.1.1 The Role of Chatbots in Mental Healthcare
10.1.2 Customizing and Providing Emotional Support via Chatbots
10.1.3 Technical Foundations of Mental Health Chatbots
10.1.4 Challenges and Ethical Considerations
10.2 Methodology
10.2.1 The Importance of Dataset Quality for Mental Healthcare Chatbots
10.2.2 Dataset Collection
10.2.3 Data Labeling for Sentiment and Intent Recognition
10.2.4 Intent Recognition
10.2.5 Data Preprocessing
10.2.5.1 Tokenization
10.2.5.2 Stop Word Removal
10.2.5.3 Lemmatization
10.2.5.4 Sentiment Labeling
10.2.6 Fine-Tuning of NLP Model
10.2.7 Model Selection
10.2.8 Transfer Learning
10.2.9 Sentiment Analysis
10.2.10 Intent Detection
10.2.11 Model Training and Evaluation
10.3 Continuous Improvement
10.4 Proposed Work Modules
10.4.1 Smart Interfaces
10.4.1.1 User Interface (UI)
10.4.1.2 Natural Language Processing (NLP) Model
10.4.1.3 Continuous Learning
10.5 Results and Discussion
10.5.1 Inferences
10.5.1.1 Accuracy Graph for Sentiment Analysis
10.5.1.2 Intent Recognition Results
10.5.1.3 ChatBot Response Accuracy Over Time
10.5.1.4 Comparison with Related Work
10.6 Conclusion and Future Work
10.6.1 Future Work
References
11. Medical Sensor Network, Edge Computing, and Blockchain for Efficient Medical Data Exchange
Amit Kumar Tyagi and Richa
11.1 Introduction to Medical Sensor Network, Blockchain Technology and Edge Computing: Challenges and Future Scope
11.1.1 Challenges
11.1.2 Future Scope
11.2 Background Work
11.3 Medical Sensor Networks in Healthcare: Overview, Types, Importance and Applications
11.4 Edge Computing in Healthcare: Definition, Concepts, Advantages and Challenges and Opportunities
11.5 Blockchain Technology in Healthcare: Principles, Use Cases and Benefits and Challenges
11.5.1 Popular Issues of Security and Privacy in Healthcare
11.6 Blockchain, Medical Sensor Networks, and Edge Computing Integration for a Safer, Secure Healthcare Services
11.6.1 Challenges in Integration of Blockchain, Medical Sensor Networks, and Edge Computing
11.6.2 Benefits and Impacts of Blockchain, Medical Sensor Networks, and Edge Computing Integration
11.7 Use Cases and Applications of Emerging Technology in Medical Data Exchanges
11.8 Issue and Challenges Toward Using Technologies in Healthcare Sector
11.8.1 Legal, Regulatory, Security, and Privacy Issue in Medical Data Exchange
11.8.2 Patient Consent and Control Required in Medical Sector (Using Emerging Technology)
11.8.3 Technical and Nontechnical Challenges Toward Using Emerging Technologies in Medical Data Exchanges
11.9 Future Research Opportunities Toward Using Edge Computing, Blockchain, and Medical Sensor Networks in Healthcare Sector
11.10 Conclusion
References
12. Impact of AI on Robotics Surgery: A Comprehensive Overview
M. Ramprasath, Elangovan G., Prakash Duraisamy and V. Kavitha
12.1 Introduction
12.1.1 Ethical Challenges in Robotic Surgery
12.1.2 Investigative Inquiry in Robotic Surgery
12.2 Related Works
12.2.1 Investigation about Robotic Surgery
12.2.2 Different Level in Robotic Technologies
12.2.3 Advancements in AI-Enhanced Operational Robotics Technologies
12.3 Exploring the Moral and Legal Environment of RAS
12.4 Computer Vision and Neurological Surgery
12.4.1 Role of AI and ML in Robotic Technologies
12.4.2 Research Area in Robotic System
12.5 Neural Surgery and Robotics
12.5.1 Classification of Surgical Robotic Systems
12.5.2 Neurosurgical Robots
12.6 Conclusion
References
13. Enhancing Healthcare and Monitoring Systems with Wearable Devices
Shrikant Tiwari, Kanchan Naithani and Ramesh Wadawadagi
13.1 Introduction
13.1.1 Overview of the Current Market and Trends in Wearable Technology
13.2 Technology Behind Wearable Devices
13.2.1 Types of Sensors Used in Wearable Devices
13.2.2 Data Acquisition and Processing
13.3 Wireless Connectivity and Integration with Other Devices
13.3.1 Benefits Wearable Technology
13.4 Applications of Wearable Devices in Healthcare
13.4.1 Fitness Tracking and Activity Monitoring
13.4.2 Monitoring of Vital Signs
13.4.3 Sleep Tracking and Analysis
13.4.4 Chronic Disease Management
13.5 Design and Development of Wearable Devices
13.5.1 User-Centered Design Principles
13.5.2 Challenges in Developing Wearable Devices for Healthcare
13.5.3 Regulatory Considerations for Medical Devices
13.6 Data Analysis and Interpretation
13.6.1 Machine Learning Algorithms for Predictive Analysis
13.7 Ethical and Legal Considerations
13.7.1 Liability and Responsibility of Device Manufacturers and Healthcare Providers
13.7.2 Legal Frameworks and Regulations for Wearable Devices in Healthcare
13.8 Future Directions and Opportunities
13.8.1 Potential Applications of Wearable Devices in Healthcare
13.8.2 Challenges and Future Directions for Research and Development
13.9 Conclusion
References
14. Surgical Robots in Smart Hospitals: Enhancing Precision and Patient Outcomes
Arun Kumar Singh, Juhi Singh, Shishir Singh Chauhan and Ankit Chauhan
14.1 Introduction
14.1.1 Surgical Robotics and Sustainable Healthcare
14.1.2 Technological Advancements and System Capabilities
14.1.3 Smart Hospitals: The Digital Transformation of Healthcare
14.1.4 Convergence of Surgical Robotics and Smart Hospital Technologies
14.2 Background and Literature Review
14.2.1 Recent Research Trends and Clinical Outcomes
14.2.2 Case Studies
14.2.2.1 Integration of Surgical Robots with Smart Hospital Systems
14.2.2.2 Emerging Applications in Specialized Surgeries
14.2.2.3 Data-Driven Outcome Improvement
14.2.2.4 Patient-Centric Outcomes
14.2.3 Evaluation of Surgical Robotic Systems
14.3 Smart Hospitals: The Ecosystem of the Future
14.3.1 The Role of Data and Connectivity
14.3.2 Technological Underpinnings of Surgical Robotics
14.3.3 Advanced Imaging and Sensor Integration
14.3.4 Software, AI, and Machine Learning
14.3.5 Networked Systems and Interoperability
14.3.6 Cybersecurity and Data Integrity
14.3.7 Training and Collaborative Environments
14.3.8 Enhancing Precision through Robotics
14.4 Proposed Methodology for Integration
14.4.1 System Architecture
14.5 Advancing Sustainable Development Goals
14.6 Conclusion and Statistical Analysis
14.7 Future Research Directions
References
15. Integrating Blockchain and Telerobotic in Orthopedics: Advancing Secure and Remote Surgical Innovations
A. Ritu and A. Eshaan
15.1 Introduction
15.1.1 Various Challenges in Blockchain Designed for Robotics
15.2 Related Work
15.3 Proposed Methodology
15.3.1 VeChain (Supply Chain Blockchain for Medical Devices)
15.3.2 Algorand (Scalable Blockchain for AI and Healthcare)
15.4 Conclusion
References
16. Improving Lung Cancer Detection Using Hybrid Features and Optimized through VGG-16
A. Ritu and A. Eshaan
16.1 Introduction
16.1.1 Problem Statement and Objectives
16.2 Literature Review
16.3 Material and Methods Used
16.3.1 VGG Net 16 Architecture
16.3.1.1 Dense_Net
16.3.1.2 Transition Layers
16.3.1.3 Bottleneck Layers
16.3.2 Evaluation Metrics
16.4 Results and Discussion
16.5 Conclusion and Future Scope
References
Part 3: Issues and Challenges Towards Implementing Surgical Robots in Smart Hospitals
17. Education and Certification for Surgical Robot Operators
V. Shyamala Susan, K. Chitra Chellam, K.S. Anushya and Ir. Bambang Sugiyono Agus Purwono
17.1 Introduction
17.2 Literature Survey
17.3 Pathways Leading to Becoming a Surgical Robot Operator
17.3.1 Prerequisite for Education
17.3.2 Academic and Institutional Training Programs
17.4 Hospital Privileging and Credentialing
17.4.1 The Significance of Credentials and Privileges Gained from a Hospital
17.4.2 Basic Aspects Relating to the Credentialing and Privileging Process Undertaken by Hospitals
17.5 Recredentialing and Ongoing Competency Evaluation
17.5.1 Issues with Robotics Surgical Credentialing
17.6 Certification Requirements for Surgical Robot Operators
17.6.1 International Certification Bodies
17.6.2 Primary International Certification Authorities
17.6.3 Certification Process
17.7 Continuing Medical Education (CME) and Skill Maintenance
17.7.1 Advanced Training Courses on New Robotic Platforms
17.7.1.1 The Perks of Further Education
17.7.1.2 Types of Advanced Training Programs
17.7.1.3 Consequences of Supplementary Education on the Outcomes of Surgery
17.7.2 Participation in Conferences on Robotic Surgery
17.7.2.1 The Importance of Conferences Somewhere in the Interconnected World
17.7.2.2 Lead in Robotic Surgery Conferences
17.7.2.3 Advantages of Participation and Conferences
17.7.3 Taking Part in Research and Clinical Studies
17.7.3.1 The Significance of the Research in the Field of Robotic Surgery
17.7.3.2 Various Forms of Research Involvement
17.7.3.3 Outcomes of Research Participation
17.7.4 Regular Skills Reassessment via Simulation Modules
17.7.4.1 Why Skills Reassessment is Important
17.7.4.2 Types of Simulation Training
17.7.4.3 Impact of Regular Reassessment
17.8 Robotic Surgery Training Issues and Evolving Directions
17.8.1 Challenges in Training and Certification
17.8.1.1 High Cost of Training
17.8.1.2 Limited Access to Training Centers
17.8.1.3 Differences in Certification Requirements
17.8.1.4 Evolution of Technologies that Must Be Continuously Mastered
17.8.2 Future Trends in Robotic Surgery Education
17.8.2.1 AI-Assisted Training
17.8.2.2 Virtual and Remote Reality Training
17.8.2.3 Standardized Global Certification Programs
17.8.2.4 Multidisciplinary Training Expansion
17.9 Ethical Considerations in Robotic Surgery Training
17.9.1 Patient Safety
17.9.2 Informed Consent
17.9.3 Justifiable Distribution of Training Opportunities
17.9.4 Adherence to Policies
17.10 Conclusion
References
18. Enhancing Healthcare Cybersecurity with AI-Driven Threat Intelligence: Proactive Defense against Evolving Cyber Threats
Emmanuel Innocent Umoh, Hussaini Bishara and Avinash Kumar Sharma
18.1 Introduction
18.1.1 Major Elements Involved with AI-Based Threat Intelligence Integration in Healthcare Cybersecurity
18.2 The Evolving Healthcare Threat Landscape
18.2.1 Persistent Healthcare Cybersecurity Risks
18.2.2 Increased Use of AI-Driven Cyberattacks in Healthcare
18.2.3 New Cybersecurity Threats and their Impact on Patient Safety
18.2.4 The Role of AI in Identifying and Mitigating Cyber Threats
18.3 AI-Driven Threat Intelligence Framework for Healthcare
18.3.1 Comprehending the Architecture of AI-Driven Cybersecurity in Healthcare
18.3.1.1 Data Collection Layer
18.3.1.2 Threat Intelligence Engine
18.3.1.3 Automated Incident Response System
18.3.1.4 Federated Learning: Privacy-Preserving Security Intelligence
18.3.2 Machine Learning Techniques for Threat Detection
18.3.2.1 The Use of Machine Learning in Healthcare Cybersecurity
18.3.2.2 Machine Learning Techniques for Threat Detection in Healthcare
18.3.3 Applications of Machine Learning in Healthcare Cybersecurity
18.3.3.1 Fraud Detection in Medical Billing
18.3.3.2 Securing IoMT Devices
18.3.3.3 Identity and Access Management Improvement
18.4 AI-Driven Cybersecurity in Healthcare: Strategies, Implementation, and Case Studies
18.4.1 Integration with Healthcare IT Infrastructure
18.4.1.1 AI in Zero Trust Security Architecture
18.4.1.2 Threat Hunting Based on AI
18.4.2 Challenges in AI Implementation
18.4.3 Real-World Applications of AI in Healthcare Cybersecurity
18.4.3.1 Case Study 1: How AI Stopped a Ransomware Attack in a Hospital Network
18.4.3.2 Case Study 2: Anomaly Detection Using Artificial Intelligence in Medical Device Security
18.4.3.3 Case Study 3: Predictive Analytics for Early Threat Detection in Healthcare Cloud Services
18.4.4 Performance Indicators for AI-Driven Cybersecurity
18.4.4.1 Detection Accuracy, Precision-Recall Tradeoffs, and Response Time
18.4.4.2 Reduction in False Positives Compared to Traditional Methods
18.4.4.3 AI’s Impact on Threat Mitigation Time and Resource Efficiency
18.5 Future Directions and Proposed Model to Enhance Healthcare Cybersecurity
18.5.1 The Future of AI-Powered Security in Indian Healthcare
18.5.1.1 AI-Powered Security Collaboration for Healthcare
18.5.1.2 Blockchain-AI Synergy for Cybersecurity
18.5.1.3 Explainable AI (XAI) for Transparent Cybersecurity Decisions
18.5.2 Proposed Model: Adaptive AI-Blockchain Threat Defense System (ABTDS)
18.5.3 Policy and Regulatory Considerations
18.6 Conclusion
References
19. Advancements and Challenges in IoT Adoption for Healthcare: A Review
Ashok Kumar Jayaraman, Gayathri Ananthakrishnan, Anugna Yakkala, Pavan Tej P. G. and Tina Esther Trueman
19.1 Introduction
19.2 IoT Communication Models
19.2.1 Device to Device
19.2.2 Device to Cloud
19.2.3 Device to Gateway
19.2.4 Multimodal
19.3 Issues and Challenges in Medical Domain
19.3.1 Security and Privacy
19.3.2 Interoperability and Standards
19.3.3 Legal, Regulatory, and Rights
19.4 Artificial Intelligence of Things (AIoT)
19.5 Conclusion
References
Part 4: Near Future Development Towards Using Surgical Robots in Smart Hospitals
20. Intelligent Medical Business Assistant: Transforming Hospital Efficiency Through Advanced Analytics
Ahmed A. Esmail, Sayed M. Abdallah, Mohamed A. Elsayed, Ahmed E. Mohamed, Mostafa H. Fawzy, Ahmed Ismail Ebada and Aya M. Al-Zoghby
20.1 Introduction
20.2 Related Work
20.2.1 Existing Systems
20.2.2 Overall Problems of Existing Systems
20.2.3 Comparison Between Existing and Proposed Method
20.3 Methodology
20.4 Results and Dataset
20.5 Conclusion
References
21. Next-Generation Industrial Robots: Design and Development
Yash Suhas Jawale, Praveen Joe I. R. and Vijay John
21.1 Introduction
21.1.1 Present Conditions
21.2 Future Requirements
21.2.1 New Materials
21.2.2 Newer and Faster Processing Units
21.2.3 Customizable Hardware Via FPGA Implementation
21.3 Conclusion
References
22. A Deep Learning–Based Expression Recognition Analysis Assistant for Psychologists
Harjyot Singh Bagga, Sanjoi Sethi, Rishab Goswami, Annapurna Jonnalagadda and Ushus E.Z.
22.1 Introduction
22.2 Background
22.2.1 Research Description and Goals
22.3 Technical Specification
22.3.1 Text
22.3.2 Audio
22.3.3 Video
22.4 Design Approach and Details
22.4.1 Design Approach/Materials and Methods
22.4.2 Preparing the Data
22.4.2.1 Text
22.4.2.2 Audio
22.4.2.3 Video
22.4.3 Constraints and Trade Offs
22.4.4 Hardware and Software Requirements
22.5 Result Screenshots
22.5.1 Text
22.5.2 Audio
22.5.3 Video
22.5.4 Website Screenshots
22.5.5 Results and Discussion
22.6 Conclusions
References
23. Future Directions for Surgical Robots in Smart Hospitals
A. Sivasangari, V.J.K. Kishore Sonti, J. Cruz Antony, E. Murali and D. Deepa
23.1 Introduction
23.2 Increased Influence of Robotics in Healthcare
23.3 AI’s Role in Evolution of Modern Healthcare Industry
23.4 Integration of Robotics and AI in the Development of Advanced Surgical Robots
23.5 Smart Hospitals: From Ease of Registration to Remote Surgery and Teleoperated Haptic Technology
23.6 Futuristic Implications of Surgical Robots
23.7 Conclusion
References
24. The Prospective of Artificial Intelligence-Integrated Robotics Towards Orthopedics and Neurosurgery
Chandana Mohanty, Priya Das, Swati Swayamsiddha and Sarita Nanda
24.1 Introduction
24.1.1 Importance of AI-Robotics Toward Orthopedics
24.1.2 Importance of AI-Robotics Toward Neurosurgery
24.1.3 Organization of the Chapter
24.2 AI Robotics
24.2.1 Artificial Intelligence
24.2.1.1 Machine Learning
24.2.1.2 Neural Network
24.2.1.3 Deep Learning
24.2.1.4 Natural Language Processing
24.2.2 Robotics
24.2.2.1 Design
24.2.2.2 Action
24.2.2.3 Perception
24.2.3 AI for Human–Robot Interaction
24.3 Applications of AI-Robotics in Orthopedics Field
24.3.1 Humanoid/Robot-Assisted Orthopedics Surgery
24.3.2 Autonomous Vehicles for the Movement of Patient
24.3.3 Replacement of Caregivers and Nurses
24.3.4 Virtual Physical Therapy and Telerehabilitation
24.3.5 Radiography Analysis
24.3.6 Chatbots
24.3.7 Nanobots
24.4 Applications of AI Robotics in Neurosurgery Field
24.4.1 AI Robotics in Cranial Neurosurgery
24.4.1.1 Tumor Resection
24.4.1.2 Deep Brain Stimulation (DBS)
24.4.1.3 Intracranial Hemorrhage Management
24.4.2 AI Robotics in Spinal Neurosurgery
24.4.2.1 Spinal Fusion Surgery
24.4.2.2 Minimally Invasive Spine Surgery
24.4.2.3 Spinal Cord Injury Rehabilitation
24.4.3 AI-Robotics in Pediatric Neurosurgery
24.4.3.1 Congenital Malformation Corrections
24.4.3.2 Epilepsy Surgery
24.4.4 AI Robotics in Neurovascular Surgery
24.4.4.1 Aneurysm Clipping and Coiling
24.4.4.2 Stroke Management
24.4.5 AI Robotics in Neurorehabilitation
24.4.5.1 Robotic Exoskeletons
24.4.5.2 AI-Driven Neurofeedback Systems
24.4.6 AI-Robotics in Neurosurgical Training
24.4.6.1 Virtual and Augmented Reality Simulations
24.4.6.2 AI-Assisted Skill Assessment
24.5 Potential Impact of AI Robotics and Future Implications
24.6 Challenges and Further Research Directions
24.7 Conclusion
References
Appendix A
25. Innovative Approaches in Smart Healthcare: Artificial Intelligence Techniques Via Federated Learning
Shrikant Tiwari, Kanchan Naithani and Ramesh S. Wadawadagi
25.1 Introduction
25.1.1 Overview of Smart Healthcare Systems
25.1.1.1 IoT in Healthcare
25.1.1.2 Artificial Intelligence in Healthcare
25.1.1.3 Wearable Devices and Mobile Applications
25.1.1.4 Telemedicine and Remote Consultations
25.1.1.5 Electronic Health Records (EHR)
25.1.2 Key Challenges in Healthcare Data Management
25.2 Foundations of Artificial Intelligence in Healthcare
25.2.1 Current Trends and Developments
25.3 Federated Learning in Healthcare
25.3.1 Understanding Federated Learning
25.3.2 Advantages and Challenges in Healthcare Context
25.3.3 Advantages of Federated Learning in Healthcare
25.3.4 Challenges in Healthcare Federated Learning
25.3.5 Privacy Concerns in Healthcare Data
25.3.6 Techniques for Ensuring Patient Privacy in Federated Learning
25.4 Decentralized Model Training
25.4.1 Collaborative Model Training in Healthcare
25.4.2 Benefits of Decentralized Approaches
25.4.3 Innovative AI Techniques for Healthcare
25.4.4 Integration of AI Algorithms in Smart Healthcare Systems
25.5 Case Studies
25.5.1 Predictive Maintenance for Medical Devices
25.5.2 Disease Prediction Using Decentralized Data
25.5.3 Personalized Treatment Plans in Oncology
25.5.4 Impact and Outcomes of AI Techniques in Smart Healthcare
25.5.5 Security Measures in Federated Learning
25.5.5.1 Ensuring Data Security in Distributed Environments
25.5.5.2 Cybersecurity Measures for Healthcare Systems
25.6 Interoperability and Standards
25.6.1 Achieving Interoperability in Smart Healthcare
25.6.2 Standardization Efforts for Federated Learning in Healthcare
25.6.3 Regulatory and Ethical Considerations
25.6.3.1 Ethical Implications of AI and Federated Learning in Healthcare
25.7 Future Directions and Emerging Technologies
25.7.1 Prospects of AI and Federated Learning in Healthcare
25.7.2 Emerging Technologies Shaping the Future of Smart Healthcare
25.8 Conclusion
References
Index
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