Trends of Artificial Intelligence and Big Data for E-Health

Introduction
Contents
About the Editors
1 AI and Big Data for Intelligent Health: Promise and Potential
	1 Introduction
	2 Artificial Intelligence
		2.1 Machine Learning
	3 Big Data
	4 AI and Big Data in Healthcare
		4.1 Core Messages
		4.2 Short Expert Opinion
	References
2 AI and Big Data for Cancer Segmentation, Detection and Prevention
	1 Introduction
		1.1 Big Data and Artificial Intelligence (AI)
		1.2 Cancer Image Segmentation
		1.3 Cancer Detection
		1.4 Protein Structure Prediction
		1.5 Cancer Prevention
	2 Conclusions
	References
3 Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging
	1 Introduction
	2 AI and Radiology
	3 Radiology and Big Data Industry for Medical Imaging
		3.1 Technologies and Tools
			Case Example (RadMonitor, BigDataBench)
		3.2 Radiology for Data Mining Radiology and Storage
		3.3 Radiology and Dark Data Exploration
	4 Radiology and Machine Learning
		4.1 Explainable Artificial Intelligence (XIA) for Radiology
		4.2 Artificial Intelligence of Things (AIoT) (AIOT) for Radiology
	5 Conclusion
	References
4 Neuroradiology: Current Status and Future Prospects
	1 Introduction
	2 AI in Neuroradiology
		2.1 Overview of Articles and Main CNS Subjects
		2.2 A Systematic Review of Applications Already Available
		2.3 Main Review Articles Published
	3 Conclusion
	References
5 Big Data and AI in Cardiac Imaging
	1 Introduction
	2 Data Management
	3 Algorithm Design
	4 Validation and Implementation
	5 Implementation in Cardiac Imaging
		5.1 Echocardiography
		5.2 Cardiac Magnetic Resonance Imaging
		5.3 CT
	6 Nuclear Medicine
	7 Challenges and Pitfalls
	8 Future Directions
	References
6 Artificial Intelligence and Big Data for COVID-19 Diagnosis
	1 Introduction
	2 COVID-19 Therapy and Health Informatics: Promises and Challenges
	3 COVID-19 Infrastructures and Technological Solutions
	4 The Post-COVID-19 Era and e-Health
	5 Medical Digital Transformation by the COVID-19 Pandemic
	6 Artificial Intelligence (AI) and Supply IT Infrastructure During COVID-19
		6.1 Classification for COVID-19
		6.2 Segmentation for COVID-19
		6.3 COVID-19 Risk Assessment and Prognosis
	7 Big Data Management and IT Infrastructure During COVID-19
	8 Conclusion
	References
7 AI and Big Data for Drug Discovery
	1 Introduction
	2 Big Data in Drug Discovery
	3 From Machine Learning to Deep Learning: AI Milestone
	4 Recent Novel Targets by AI
		4.1 PaccMann
		4.2 INtERAcT
		4.3 PIMKL
	5 Deep Neural Networks
	6 AI in 3D‑Pharmacophore Models
	7 AI and Gene Profiling Analysis
	8 AI and Drug Screening
	9 AI and Drug Delivery
	10 AI in Clinical Trials
	11 Conclusions
	References
8 Blockchain Technologies for Internet of Medical Things (BIoMT) Based Healthcare Systems: A New Paradigm for COVID-19 Pandemic
	1 Introduction
	2 IoMT Concept for COVID-19
	3 Artificial Intelligence and Big Data Technology in IoMT for Covid-19 Management
	4 Blockchain Concept for COVID-19
	5 HealthBlock: A Secure Blockchain-Based Healthcare Data Management System
	6 IOMT and Supply Chain for Covid-19 Patient Integration: Towards Chain of Medical Things (COMT)
	7 Blockchain IoMT (BIoMT): A New Paradigm for Healthcare Security and Issues Related to the Internet of Things
	8 Conclusion
	References
9 AI and Big Data for Therapeutic Strategies in Psychiatry
	1 Introduction
	2 Artificial Intelligence in Psychiatric Disease
		2.1 Machine Learning for Psychiatric Diseases
		2.2 Opportunities
		2.3 Challenges
		2.4 Deep Learning Models for Psychiatric Disorders
	3 Big Data in Psychiatry
		3.1 Challenges of Big Data in Psychology
		3.2 Opportunities of Big Data in Psychiatry
	4 Datasets and Tools
	5 Usage of AI Models in Psychiatry
	6 Conclusions
	References
10 Distributed Learning in Healthcare
	1 Introduction
		1.1 Caveats of Central Learning
		1.2 Motivation for Distributed Learning
	2 Distributed Learning Methods
		2.1 Global Ensemble
		2.2 Parameter Aggregation
		2.3 Traveling Model
		2.4 Split Learning
	3 Challenges and Considerations
		3.1 Heterogenous Data Distributions
		3.2 Computational and Communication Costs
		3.3 Privacy and Security
		3.4 Model Interpretability and Fairness
	4 Outlook
	5 Core Messages
	Acknowledgements
	References
11 Cybersecurity in Healthcare
	1 Introduction
	2 Introduction to Health Information Technology (HIT)
		2.1 The Electronic Health Record
		2.2 Complicating Factors
		2.3 Data
		2.4 Trends
		2.5 Health Information Technology
	3 Regulatory Compliance and Standards
		3.1 HIPAA
		3.2 GDPR
		3.3 Compliance
		3.4 Security Controls
		3.5 De-identification
		3.6 Transfer
		3.7 Devices
		3.8 Labelling
	4 Information Security and Data Privacy
		4.1 Software Development
		4.2 Third Parties
		4.3 Privacy
		4.4 Transformation
		4.5 Industry Standards
		4.6 Ethics
	5 Hardware and Software-Based Security Models
		5.1 Legacy Software
		5.2 Identity Management
		5.3 Network Security
		5.4 Audit and Logging
		5.5 Solutions
			Distributed Privacy
			Federated Learning
	6 Security and Privacy Implications of Artificial Intelligence
		6.1 Languages and Platforms
		6.2 Testing
		6.3 Implementation
	7 Conclusion
	References
12 Radiology and Radiomics: Towards Oncology Prediction with IA and Big Data
	1 Introduction
	2 Big Data for Oncology Management
	3 Areas of Radiomics/Radiogenomics
	4 Neuroradiology Applications
	5 Breast Imaging Applications
	6 Lung Imaging Applications
	7 Gastrointestinal Tract Imaging Applications
	8 Radiotherapy Implications
	9 Conclusion
	References
General Conclusion