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