Artificial Intelligence for Health 4.0: Challenges and Applications

Cover
Title Page
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgment
List of Contributors
List of Figures
List of Tables
List of Abbreviations
Chapter 1: Healthcare 4.0: A Systematic Review and Its Impact Over Conventional Healthcare System
1.1: Introduction
1.1.1: Application scenarios of healthcare 4.0
1.1.2: The architecture of healthcare 4.0
1.1.3: Requirements and characteristics of healthcare 4.0
1.2: Evolution of Healthcare
1.3: Need of Healthcare 5.0
1.4: Advances in the Healthcare Industry
1.4.1: M-Healthcare
1.4.2: Healthcare data of patients
1.4.3: IoT and healthcare
1.4.4: Blockchain technology and healthcare
1.4.5: Big data analytics and healthcare
1.5: Telemedicine Services
1.5.1: Big data and IoT for healthcare 4.0
1.5.2: Blockchain and healthcare 4.0
1.5.3: AI and healthcare 4.0
1.5.4: Cyber–physical system and healthcare 4.0
1.5.5: Smart medical devices
1.6: Opportunities and Challenges Involved in Healthcare
1.7: Future Scope and Trends
1.8: Conclusion
1.9: Acknowledgment
1.10: Funding
1.11: Conflict of Interest
References
Chapter 2: Data Imaging, Clinical Studies, and Disease Diagnosis using Artificial Intelligence in Healthcare
2.1: Introduction
2.1.1: Classifications of artificial intelligence
2.1.1.1: Machine learning: Deep learning and neural network
2.1.1.2: Rule-based expert systems
2.1.1.3: Physical robots and software robotics
2.2: Machine Learning for Typical Biomedical Data Types
2.2.1: Data from multiple omics
2.2.2: Integration based on data
2.2.3: Incorporating models
2.2.4: Data on behavior
2.2.5: Data from video and conversations
2.2.6: Mobile sensor data
2.2.7: Data on the environment
2.2.8: Pharmaceutical research and development data
2.2.8.1: Chemical compounds
2.2.8.2: Clinical trials
2.2.9: Unintentional reports
2.2.10: Literature in biomedicine data
2.3: Application of AI
2.3.1: Biomedical information processing
2.3.2: AI for living support
2.3.3: Biomedical research
2.3.4: Medicine
2.3.5: Cancer and miscellaneous
2.4: Assessment of AI Applications in Healthcare
2.4.1: Phase 0
2.4.2: Phase 1
2.4.3: Phase 2
2.4.4: Phase 3
2.4.5: Phase 4
2.5: Artificial Intelligence’s Challenges in the Use of Pharmaceutical R&D Data
2.6: Future Directions for AI in Healthcare
2.6.1: Analytical integration
2.6.2: Transparency in models
2.6.3: Model security
2.6.4: Learning that is federated
2.6.5: Data errors
2.7: Conclusion
2.8: Acknowledgment
2.9: Funding
2.10: Conflicts of Interest
References
Chapter 3: Leveraging Artificial Intelligence in Patient Care
3.1: Introduction
3.2: Advancement in Artificial Intelligence
3.2.1: AI spring: artificial intelligence’s inception
3.2.2: AI summer and winter: Artificial intelligence’s highs and lows
3.2.3: AI’s fall: The harvest
3.2.4: The future: The importance of regulation
3.3: Artificial Intelligence’s Health Benefits
3.3.1: Advantages
3.4: Application
3.4.1: Cardiology
3.4.2: Applications of artificial intelligence in the medical field
3.4.3: Image and disease diagnosis using artificial intelligence
3.5: Recent Advancements in the Field of Artificial Intelligence
3.5.1: For medical imaging, the use of artificial intelligence is essential
3.5.2: Artificial intelligence science and technology
3.6: Artificial Intelligence and its Applications in Diagnostics
3.6.1: Sets of data
3.6.2: A medical image’s preprocessing
3.6.3: Optimization of models and parameters based on improved data
3.6.4: The principal component analysis (PCA)
3.6.5: Analyzing medical images using artificial intelligence
3.6.6: Imaging the brain via artificial intelligence
3.6.7: Chest imaging with artificial intelligence
3.6.8: In breast imaging, artificial intelligence is being used
3.6.9: The use of AI in cardiac imaging
3.6.10: Artificial intelligence in bone imaging
3.6.11: The use of Artificial Intelligence (AI) in stroke imaging
3.6.12: Using AI to treat diseases of the lungs
3.6.13: Artificial intelligence in the treatment of cancer
3.7: Conclusion
3.8: Acknowledgment
3.9: Funding
3.10: Conflicts of Interest
References
Chapter 4: Patient Monitoring Through Artificial Intelligence
4.1: Introduction
4.2: Purpose of Patient Monitoring
4.2.1: Patient monitoring involvement in today’s healthcare
4.2.2: Improving healthcare outcomes by using patient monitoring
4.3: Wearable Patient Monitoring Sensors
4.3.1: Wireless health monitoring specifications
4.3.2: Different types of sensors
4.4: Involvement of AI in Patient-Monitoring
4.4.1: Mobility aids the living environment
4.4.2: Clinical decision-making assistance
4.4.3: Smartphones, apps, sensors, and devices
4.4.4: Processing of text language
4.4.5: Healthcare applications of text processing technology
4.4.6: Using consumer technology to its full potential
4.4.7: AI’s function in diabetes forecasts and management
4.4.7.1: Apps and technologies for diabetes monitoring
4.5: AI-Assisted Monitoring of the Heart
4.5.1: AI in cardiology with virtual applications
4.5.2: Supporting system in clinical decisions
4.5.3: Augmented reality (AR), virtual reality (VR), and virtual assistants
4.5.4: Automated analysis with data
4.6: Neural Applications Linked to AI and Patient Monitoring
4.6.1: AI for dementia patients
4.6.2: Dementia monitoring
4.6.3: Supporting dementia patients
4.7: AI for Migraine Patients
4.8: Conclusion
4.9: Acknowledgment
4.10: Funding
4.11: Conflict of Interest
References
Chapter 5: Artificial Intelligence: A Promising Approach Toward Targeted Drug Therapy in Cancer Treatment
5.1: Introduction
5.2: AI, Machine Learning, and Deep Learning
5.3: Drug Development Process
5.3.1: Role of AI in chemotherapy
5.3.2: Role of AI in radiotherapy
5.3.3: Role of AI in cancer drug development
5.3.4: Role of AI in immunotherapy
5.4: Monoclonal Antibodies (mAbs) used in Cancer Treatment
5.5: MOA of mAbs
5.6: Future Prospects
5.7: Conclusion
5.8: Acknowledgment
5.9: Funding
5.10: Conflicts of Interest
References
Chapter 6: Artificial-Intelligence-Based Cloud Computing Techniques for Patient Data Management
6.1: Introduction of Artificial Intelligence Based Cloud Computing Techniques
6.2: Cloud Computing: A New Economic Computing Model
6.2.1: Infrastructure as a service (IaaS)
6.2.2: Platform as a service (PaaS)
6.2.3: Software as a service (SaaS)
6.3: The US National Institute of Standards and Technology (NIST) has Identified four Models for Cloud Computing Deployment
6.3.1: Public cloud
6.3.2: Community cloud
6.3.3: Private cloud
6.3.4: Hybrid cloud
6.4: Cloud Computing from the Perspective of Management, Security, Technology, and Legality
6.4.1: Management aspect
6.4.2: Technology aspect
6.4.3: Security aspect
6.4.4: Legal aspect
6.5: Cloud Computing Strategic Planning
6.5.1: Stage I – Identification
6.5.2: Stage II – Evaluation
6.5.3: Stage III – Action
6.5.3.1: Step 1: Determination of cloud service and deployment model
6.5.3.2: Step 2: Obtain confirmation from a chosen cloud provider
6.5.3.3: Step 3: Take consideration in migration of future data
6.5.3.4: Step 4: Start of implementation of pilot
6.5.4: Stage IV – Follow-up
6.6: Cloud Computing Research Utilization in Healthcare
6.6.1: Cloud computing in telemedicine/teleconsultation
6.6.2: Cloud computing in public health and patient self-management
6.6.3: Cloud computing in hospital management/clinical information systems
6.6.4: Cloud computing in therapy
6.6.5: Cloud computing in secondary use of data
6.7: Conclusion
6.8: Acknowledgment
6.9: Funding
6.10: Conflict of Interest
References
Chapter 7: Role of Artificial Intelligence and Robotics in Healthcare
7.1: Introduction
7.1.1: History of artificial intelligence
7.1.2: The need for AI
7.1.3: How did AI change the way medicine was practiced in the past?
7.1.4: Types of AI
7.2: AI in Healthcare
7.2.1: AI tool
7.2.2: Natural language processing
7.2.3: Machine learning
7.2.4: Algorithms
7.2.5: Artificial neural network
7.2.6: Support vector machine
7.2.7: Deep learning
7.3: Integrating AI into Healthcare Delivery
7.3.1: Patient monitoring
7.3.2: Disease diagnostics and prediction
7.3.3: Precision medicine
7.3.4: Drug discovery
7.3.5: Dermatology
7.3.6: Coronavirus
7.3.7: AI in ophthalmology
7.3.8: Design of the treatment
7.4: The Present State of AI and Its Future
7.4.1: Benefits
7.4.2: Difficulties of AI in healthcare
7.5: Role of Robotics in Modern Healthcare
7.5.1: Drug research and development
7.5.2: Dispensing in pharmacies
7.5.3: Logistics at the hospital
7.6: Robotic Healthcare Is More Advance than Conventional Dispensing
7.6.1: Increased effectiveness
7.6.2: Medical dispensing in an error-free environment
7.6.3: Pharmaceutical operations efficiency
7.6.4: Confidentiality
7.6.5: A toxin-free and secure setting
7.6.6: Advantages of robotics in healthcare systems
7.7: Acceptance and Implementation of Robots in the Healthcare Business
7.8: AI Robotics Emerging Together to Transform the Healthcare System
7.8.1: Error rates reduction
7.8.2: Improving the health of patients
7.8.3: Early detection
7.8.4: Improving decision-making
7.8.5: Research and development
7.8.6: Treatment
7.8.7: End-of-life care regeneration
7.9: Categories of AI Robotic Systems used in the Healthcare System
7.9.1: Assistants to surgeons
7.9.2: Pharmabiotics
7.9.3: Telehealthcare
7.9.4: Robotics with exoskeletons
7.9.5: Robots for cleaning and decontamination
7.10: Robotic Programming
7.11: Conclusion
7.12: Acknowledgment
7.13: Funding
7.14: Conflict of Interest
References
Chapter 8: Artificial Intelligence and Machine Learning Approach for Development and Discovery of Drug
8.1: Introduction
8.2: Tools of AI used to Emphasize Pharmacy
8.2.1: The robot pharmacy
8.2.2: The MEDi robot
8.2.3: The erica robot
8.2.4: The TUG robots
8.3: Applications
8.3.1: Modifying drug release
8.3.2: Product development
8.4: Benefits
8.5: AI-Integrated Medicine Development
8.6: Role of Active Learning and Machine Learning in Drug Discovery
8.7: Explainable AI
8.8: Computational Approaches for Explainable AI
8.8.1: Feature attribution
8.8.2: Instance-based approach
8.8.3: Graph convolution-based approach
8.8.4: Self-explaining
8.8.5: Uncertainty estimation
8.8.6: In silico molecular modeling
8.9: AI Networks and Associated Tools
8.9.1: AlphaFold
8.9.2: DeepChem
8.9.3: ODDT
8.9.4: Cyclica
8.9.5: DeepTox
8.9.6: Deep neural net QSAR
8.9.7: Organic
8.9.8: PotentialNet
8.9.10: Hit dexter
8.10: Technical Obstacles and Prospects
8.11: Conclusion
8.12: Acknowledgment
8.13: Funding
8.14: Conflicts of Interest
References
Chapter 9: Artificial Intelligence in Boosting the Development of Drug
9.1: Artificial Intelligence
9.2: Computer-Based Intelligence in the Lifecycle of Drug Items
9.3: Drug Development
9.4: The Drug Development Process
9.5: In Drug Discovery, Artificial Intelligence
9.6: Artificial Intelligence in Drug Screening
9.6.1: The expectation of the physicochemical properties
9.6.2: Forecast of bioactivity
9.6.3: Expectation of poisonousness
9.7: Artificial Intelligence in Planning Drug Particles
9.7.1: The expectation of the objective protein structure
9.7.2: Foreseeing drug-protein communications
9.8: Artificial Intelligence in Propelling Drug Item Advancement
9.9: Artificial Intelligence in Drug Fabricating
9.10: Artificial Intelligence in Quality Assurance and Control
9.11: Artificial Intelligence in a Clinical Trial Plan
9.12: Artificial Intelligence in Drug Item Execution
9.12.1: Artificial intelligence in market situating
9.12.2: Artificial intelligence in market expectation and investigation
9.13: Artificial Intelligence in the Item Cost
9.14: Conclusion
9.15: Acknowledgment
9.16: Funding
9.17: Conflict of Interest
References
Chapter 10: Artificial Intelligence in Medical Image Processing
10.1: Introduction
10.2: Magnetic Resonance Imaging (MRI)
10.2.1: Alzheimer’s disease (AD)
10.2.2: Skeletal issues
10.2.3: Brain illness diagnosis
10.2.4: Cancer and other disease analysis
10.3: Radiography-Based COVID-19 Diagnosis
10.3.1: ML-based approach
10.3.2: DNN algorithms for diagnosis
10.3.2.1: DNN and chest CT scan
10.3.2.2: DNN and chest X-ray
10.3.2.3: New DNN models on chest CT scan
10.3.2.4: New DNN models on chest X-ray
10.3.3: Transfer learning (TL) approach
10.3.3.1: Implementing TL approach on chest CT scan
10.3.3.2: Implementing TL approach on chest X-ray
10.3.3.3: Smartphone apps
10.4: Echocardiogram Analysis and Classification using AI
10.5: AI-Based Ultrasound Imaging Analysis
10.6: Conclusion
10.7: Acknowledgment
10.8: Funding
10.9: Conflict of Interest
References
Chapter 11: Advancement of AI in Cancer Management: Role of Big Data
11.1: Introduction
11.1.1: Big data
11.2: The Source and Type of Big Data, and Their Concern
11.2.1: The challenge of big data
11.3: Big Data Sources and Platforms
11.3.1: The national population-based cancer database
11.3.2: Commercial and private cancer databases
11.3.3: Cancer biological science and various “Omic” databases
11.4: Data Collection in Big Data for Oncology Treatment
11.4.1: Data management and aggregation in big data
11.4.2: The data sources for big data in medicine
11.5: Therapy Plan for Cancer
11.5.1: Big data will aid in the development of novel cancer therapies
11.5.2: A cancer treatment plan that is tailored to each patient
11.6: Big Data Powers the Design of “Smart” Cell Therapies for Cancer
11.6.1: Instructions are inserted into the cells
11.7: Future and Challenges of Big Data in Oncology
11.7.1: Challenges
11.8: Perspectives for the Future
11.9: Conclusion
11.10: Acknowledgment
11.11: Funding
11.12: Conflict of Interest
References
Chapter 12: Targeted Drug Delivery in Cancer Tissues by Utilizing Big Data Analytics: Promising Approach of AI
12.1: Introduction
12.2: Tools and Techniques for Targeted Drug Discovery and Delivery
12.2.1: Data sources available for drug discovery
12.2.2: Anticancer drug target discovery and validation
12.3: Sources of Big Data in Drug Discovery and Delivery
12.3.1: COSMIC-3D
12.3.2: The cancer genomic atlas
12.3.3: Gene expression omnibus (GEO)
12.3.4: Human protein atlas
12.4: Big Data Analytics
12.5: Future Prospects of “Big Data Analytics”
12.6: Challenges in the Field of “Big Data Analytics”
12.7: Conclusion
12.8: Acknowledgment
12.9: Funding
12.10: Conflict of Interest
References
Index
About the Authors
About the Editors