Smart Distributed Embedded Systems for Healthcare Applications

✍ Scribed by Preeti Nagrath, Jafar A. Alzubi, Bhawna Singla, Joel J. P. C. Rodrigues, A. K. Verma

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 199
• Series: Explainable AI (XAI) for Engineering Applications
• Category: Library

✦ Synopsis

This book discusses the applications and optimization of emerging smart technologies in the field of healthcare. It further explains different modeling scenarios of the latest technologies in the healthcare system and compares the results to better understand the nature and progress of diseases in the human body, which would ultimately lead to early diagnosis and better treatment and cure of diseases with the help of distributed technology.

• Covers the implementation models using technologies such as artificial intelligence, machine learning, and deep learning with distributed systems for better diagnosis and treatment of diseases.

• Gives in-depth review of technological advancements like advanced sensing technologies such as plasmonic sensors, usage of RFIDs, and electronic diagnostic tools in the field of healthcare engineering.

• Discusses possibilities of augmented reality and virtual reality interventions for providing unique solutions in medical science, clinical research, psychology, and neurological disorders.

• Highlights the future challenges and risks involved in the application of smart technologies such as cloud computing, fog computing, IOT, and distributed computing in healthcare.

• Confers to utilize the AI and ML and associated aids in healthcare sectors in the post-Covid 19 period to revitalize the medical setup.

Contributions included in the book will motivate technological developers and researchers to develop new algorithms and protocols in the healthcare field. It will serve as a vast platform for gaining knowledge regarding healthcare delivery, health- care management, healthcare in governance, and health monitoring approaches using distributed environments. It will serve as an ideal reference text for graduate students and researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical fields.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Healthcare Engineering Using AI and Distributed Technologies
1.1 Introduction
1.2 Related Work
1.3 Impact of AI in the Healthcare Industry
1.3.1 Challenges in AI Implementation in Healthcare
1.3.2 AI Tools for Prediction of Critical Diseases
1.3.3 Benefits and Limitations of AI in Healthcare Industry
1.4 Transforming Healthcare with Distributed Computing
1.5 AI-Based Robots in the Healthcare Ecosystem
1.6 Conclusion and Future Scope
References
Chapter 2 Cloud Computing in Healthcare: A Systematic Study
2.1 Introduction
2.2 Literature Review
2.3 Medline
2.3.1 Data Extraction and Full-Text Screening
2.4 Infrastructure and Dynamic Scalability
2.5 Information Sharing
2.6 Availability
2.7 Monitoring Software for the Cloud
2.8 Biomedicine and Healthcare Benefit from Cloud Computing
2.9 Biotech Solutions in the Cloud
2.9.1 Bioinformatics Software as a Service
2.10 Cloud-Based Molecular Simulation Tools
2.11 Cloud-Based Medical Imaging Solutions
2.12 Medical Solutions in the Cloud
2.13 Fog Computing
2.14 Threats with Cloud Computing
2.15 Future Research
2.16 Conclusion
References
Chapter 3 Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface
3.1 Introduction
3.2 Related Works
3.2.1 Retrieval of Medical Information
3.2.2 Metamap and Unified Medical Language System
3.3 Pictorial Visualisation of Data
3.3.1 Spatial Representation Based on Position
3.3.2 Representations Based on Temporal-Time
3.4 Methodology for Information Extraction
3.4.1 Format Conversion Pre-Processing
3.4.2 Tokenisation
3.4.3 Removal of Stop Words
3.4.4 Metamap Setting
3.4.5 Recognition of Time-Entity Expression
3.5 Results
3.6 Conclusion and Future Work
References
Chapter 4 Investigations on RFID-Enabled Healthcare Usage and Adoption Issues
4.1 Background
4.1.1 RFID System
4.1.2 RFID Reader
4.1.3 RFID TAGS
4.2 RFID in Healthcare System
4.2.1 Inventory Tracking
4.2.2 Identify Patients
4.2.3 Patient Tracking
4.2.4 Patient Monitoring
4.2.5 Patient Drug Compliance
4.2.6 Cost Saving
4.3 RFID Adoption Issues
4.3.1 Communication Range
4.3.2 Lifetime of Active Tags
4.3.3 Microelectromechanical System (MEMS)
4.3.4 Thermoelectric Generator
4.3.5 Electromagnetic Interference
4.3.6 Technological Challenges
4.3.7 Security, Privacy, and Data Management Challenges
4.3.7.1 Spoofing
4.3.7.2 Denial of Service
4.3.7.3 Sniffing
4.3.8 Organizational and Financial Challenges
4.4 Conclusion
References
Chapter 5 Photonic Crystal Fiber Plasmonic Sensor for Applications in Medicine
5.1 Introduction
5.2 Background and Development of Photonic Crystal Fiber
5.3 Plasmonic Sensor Based on Photonic Crystal Fiber
5.3.1 Single-Coated Plasmonic Sensors
5.3.2 Double-Coated Plasmonic Sensors
5.3.3 Slotted Plasmonic Sensors
5.3.4 Miscellaneous SPR-PCF Sensor
5.4 Fabrication Techniques
5.5 Future Applications of Plasmonic Sensors
5.6 Conclusion
Author Contribution
Acknowledgement
References
Chapter 6 Augmented Reality as a Boon to Disability
6.1 Introduction: Aim and Real-Use Cases of Augmented Reality
6.1.1 Aim of Augmented Reality
6.1.2 Real-Use Cases for AR for People with Disabilities
6.2 Related Work and Discussion
6.3 Technology Advancement Using AR for Empowering Disabled People
6.3.1 AR Applications for the Physically Disabled
6.3.2 AR Applications for the Visually Disabled
6.3.3 AR Applications for the Mentally Disabled
6.4 AR Solutions for Teaching Special Students
6.4.1 Advantages of AR in Education
6.5 AR Accessible Technology to People
6.6 Conclusion
References
Chapter 7 Augmented Reality and Virtual Reality: Transforming the Future of Psychological and Medical Sciences
7.1 Introduction
7.1.1 Augmented Reality
7.1.2 Types of Augmented Reality Systems
7.1.3 Components and Aspects of AR Technology
7.2 Use of AR Technology in Medical Science
7.2.1 AR in Surgical Operations
7.2.1.1 Gastrointestinal Cancer Surgery
7.2.1.2 Knee Replacement Surgery
7.2.1.3 Brain Tumor Surgery
7.2.2 AR Therapy for ADHD
7.2.3 AR Therapy for PTSD
7.2.4 AR in Parkinson’s Disease (PD)
7.2.5 AR for Fear Classification Based on Psychological Disorder
7.2.6 AR for ASD Kids
7.3 Challenges and Opportunities
7.4 Conclusion
References
Chapter 8 Artificial Intelligence in Healthcare: Perspectives from Post-Pandemic Times
8.1 Organization of the Chapter
8.2 Introduction
8.3 Applications of Artificial Intelligence in Healthcare before the Covid-19 Pandemic
8.4 An Account of Artificial Intelligence–Guided Aids in Healthcare: Scenario from Post-Pandemic Times
8.4.1 Ethical Considerations
8.4.2 In Rapid and Effective Covid-19 Vaccine Development
8.4.3 Strategic Implications in Healthcare Sectors Using AI-Enabled Aids
8.4.4 Formulation of AI Algorithm–Based Biomarkers
8.4.5 AI-Enabled Treatments/Therapeutics against Covid-19
8.5 Discussion
8.6 Conclusion
8.7 Disclosure
References
Chapter 9 Bioweapons versus Computer-Based Counter Measure Techniques and Mathematical Modelling for the Prediction of COVID-19
9.1 Introduction and History
9.2 Genetically Engineered Pathogens
9.2.1 Designer Genes
9.2.2 Binary Bioweapon
9.2.3 Gene Therapy as Bioweapon
9.2.4 Stealth Virus
9.2.5 Hot Swapping Disease
9.2.6 Designer Disease
9.3 Counter Measure by Computer-Based Techniques
9.3.1 Computer and Artificial Intelligence–Based Counter Measure Techniques
9.3.2 Computer-Assisted Surgery as a Counter Measure
9.3.3 Big Data as Healthcare
9.3.4 Computer-Assisted Decision-Making
9.3.5 Computer Vision–Based Techniques as a Counter Measure
9.3.6 IoT-Based System as Counter Measure for Bioweapon against Crop-War
9.4 Mathematical Model for COVID-19 Prediction
9.5 Conclusion
References
Chapter 10 Evolution of Healthcare Sector and Evolving Cyberattacks—A Summary
10.1 Healthcare Industry and Its Revolution
10.2 Role of Distributed System in Healthcare
10.3 Why Healthcare is the Biggest Target for Cyberattackers?
10.4 Cyberattacks in Healthcare Industries—An Infographic
10.5 Most Frequent Attacks in Health Industries and Remedial Measures
10.6 Research Trends in Cybersecurity for the Healthcare Sector
10.7 Conclusion—Defending Healthcare Sector from Cyberattacks
References
Chapter 11 Improving Cardiovascular Health by Deep Learning
11.1 Introduction
11.2 Machine Learning
11.2.1 Machine Learning Classification
11.3 Next Generation Machine Learning
11.4 Applications of Artificial Intelligence for Cardiovascular Health
11.4.1 Precision Medicine
11.4.2 Clinical Predictions
11.4.3 Image Analysis
11.4.4 Surgical Robots
11.4.5 Health Informatics
11.5 Future Scope
11.6 Challenges
11.7 Conclusion
References
Index