Table Of ContentArtificial Intelligence for Health 4.0:
Challenges and Applications
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Artificial Intelligence for Health 4.0:
Challenges and Applications
Editors
Rishabha Malviya
Galgotias University, India
Naveen Chilamkurti
La Trobe University, Australia
Sonali Sundram
Galgotias University, India
Rajesh Kumar Dhanaraj
Galgotias University, India
Balamurugan Balusamy
Galgotias University, India
River Publishers
Published 2022 by River Publishers
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Artificial Intelligence for Health 4.0: Challenges and Applications / Rishabha
Malviya, Naveen Chilamkurti, Sonali Sundram, Rajesh Kumar Dhanaraj and
Balamurugan Balusamy.
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Contents
Preface xix
Acknowledgment xxi
List of Contributors xxiii
List of Figures xxvii
List of Tables xxix
List of Abbreviations xxxi
1 Healthcare 4.0: A Systematic Review and Its Impact Over
Conventional Healthcare System 1
Sonali Vyas, Deepshikha Bhargava, and Samiya Khan
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Application scenarios of healthcare 4.0 . . . . . . . . 3
1.1.2 The architecture of healthcare 4.0 . . . . . . . . . . . 4
1.1.3 Requirements and characteristics of healthcare 4.0 . . 5
1.2 Evolution of Healthcare . . . . . . . . . . . . . . . . . . . . 7
1.3 Need of Healthcare 5.0 . . . . . . . . . . . . . . . . . . . . 8
1.4 Advances in the Healthcare Industry . . . . . . . . . . . . . 9
1.4.1 M-Healthcare . . . . . . . . . . . . . . . . . . . . . 10
1.4.2 Healthcare data of patients . . . . . . . . . . . . . . 10
1.4.3 IoT and healthcare . . . . . . . . . . . . . . . . . . . 10
1.4.4 Blockchain technology and healthcare . . . . . . . . 10
1.4.5 Big data analytics and healthcare . . . . . . . . . . . 11
1.5 Telemedicine Services . . . . . . . . . . . . . . . . . . . . . 11
1.5.1 Big data and IoT for healthcare 4.0 . . . . . . . . . . 11
1.5.2 Blockchain and healthcare 4.0 . . . . . . . . . . . . . 12
1.5.3 AI and healthcare 4.0 . . . . . . . . . . . . . . . . . 12
1.5.4 Cyber–physical system and healthcare 4.0 . . . . . . 13
v
vi Contents
1.5.5 Smart medical devices . . . . . . . . . . . . . . . . . 14
1.6 Opportunities and Challenges Involved in Healthcare . . . . 14
1.7 Future Scope and Trends . . . . . . . . . . . . . . . . . . . 14
1.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.9 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . 15
1.10 Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.11 Conflict of Interest . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Data Imaging, Clinical Studies, and Disease Diagnosis using
Artificial Intelligence in Healthcare 19
Vandana Tyagi, Neelam Dhankher, Bhavna Tyagi,
Iidiko Csoka, and Amrish Chandra
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.1 Classifications of artificial intelligence . . . . . . . . 20
2.1.1.1 Machine learning: Deep learning and
neural network . . . . . . . . . . . . . . . 20
2.1.1.2 Rule-based expert systems . . . . . . . . . 23
2.1.1.3 Physical robots and software robotics . . . . 23
2.2 Machine Learning for Typical Biomedical Data Types . . . . 24
2.2.1 Data from multiple omics . . . . . . . . . . . . . . . 24
2.2.2 Integration based on data . . . . . . . . . . . . . . . 24
2.2.3 Incorporating models . . . . . . . . . . . . . . . . . 25
2.2.4 Data on behavior. . . . . . . . . . . . . . . . . . . . 25
2.2.5 Data from video and conversations . . . . . . . . . . 26
2.2.6 Mobile sensor data . . . . . . . . . . . . . . . . . . . 26
2.2.7 Data on the environment . . . . . . . . . . . . . . . 26
2.2.8 Pharmaceutical research and development data . . . . 27
2.2.8.1 Chemical compounds . . . . . . . . . . . . 27
2.2.8.2 Clinical trials . . . . . . . . . . . . . . . . 27
2.2.9 Unintentional reports . . . . . . . . . . . . . . . . . 28
2.2.10 Literature in biomedicine data . . . . . . . . . . . . . 28
2.3 Application of AI . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.1 Biomedical information processing . . . . . . . . . . 29
2.3.2 AI for living support . . . . . . . . . . . . . . . . . 29
2.3.3 Biomedical research . . . . . . . . . . . . . . . . . 31
2.3.4 Medicine . . . . . . . . . . . . . . . . . . . . . . . 32
2.3.5 Cancer and miscellaneous . . . . . . . . . . . . . . 35
2.4 Assessment of AI Applications in Healthcare . . . . . . . . . 35
2.4.1 Phase 0 . . . . . . . . . . . . . . . . . . . . . . . . 36
Contents vii
2.4.2 Phase 1 . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.4.3 Phase 2 . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.4 Phase 3 . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4.5 Phase 4 . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5 Artificial Intelligence’s Challenges in the Use of
Pharmaceutical R&D Data . . . . . . . . . . . . . . . . . . 38
2.6 Future Directions for AI in Healthcare . . . . . . . . . . . . 39
2.6.1 Analytical integration . . . . . . . . . . . . . . . . . 39
2.6.2 Transparency in models . . . . . . . . . . . . . . . . 39
2.6.3 Model security . . . . . . . . . . . . . . . . . . . . . 41
2.6.4 Learning that is federated . . . . . . . . . . . . . . . 41
2.6.5 Data errors . . . . . . . . . . . . . . . . . . . . . . . 42
2.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.8 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . 44
2.9 Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.10 Conflicts of Interest . . . . . . . . . . . . . . . . . . . . . . 44
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3 Leveraging Artificial Intelligence in Patient Care 57
Yogita Kumari, Khushboo Raj, Dilip Kumar Pal,
Ankita Moharana, and Vetriselvan Subramaniyan
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Advancement in Artificial Intelligence . . . . . . . . . . . . 60
3.2.1 AI spring: artificial intelligence’s inception . . . . . . 60
3.2.2 AI summer and winter: Artificial intelligence’s
highs and lows . . . . . . . . . . . . . . . . . . . . . 61
3.2.3 AI’s fall: The harvest . . . . . . . . . . . . . . . . . 62
3.2.4 The future: The importance of regulation . . . . . . . 63
3.3 Artificial Intelligence’s Health Benefits . . . . . . . . . . . . 64
3.3.1 Advantages . . . . . . . . . . . . . . . . . . . . . . 65
3.4 Application . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.1 Cardiology. . . . . . . . . . . . . . . . . . . . . . . 68
3.4.2 Applications of artificial intelligence in the
medical field . . . . . . . . . . . . . . . . . . . . . . 69
3.4.3 Image and disease diagnosis using artificial
intelligence . . . . . . . . . . . . . . . . . . . . . . 70
3.5 Recent Advancements in the Field of
Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . 72
3.5.1 For medical imaging, the use of artificial
intelligence is essential . . . . . . . . . . . . . . . . 74
viii Contents
3.5.2 Artificial intelligence science and technology. . . . . 74
3.6 Artificial Intelligence and its
Applications in Diagnostics . . . . . . . . . . . . . . . . . . 75
3.6.1 Sets of data . . . . . . . . . . . . . . . . . . . . . . 75
3.6.2 A medical image’s preprocessing . . . . . . . . . . . 76
3.6.3 Optimization of models and parameters based on
improved data . . . . . . . . . . . . . . . . . . . . . 77
3.6.4 The principal component analysis (PCA) . . . . . . . 78
3.6.5 Analyzing medical images using artificial
intelligence . . . . . . . . . . . . . . . . . . . . . . 78
3.6.6 Imaging the brain via artificial intelligence . . . . . . 79
3.6.7 Chest imaging with artificial intelligence . . . . . . . 80
3.6.8 In breast imaging, artificial intelligence is being used 80
3.6.9 The use of AI in cardiac imaging . . . . . . . . . . . 80
3.6.10 Artificial intelligence in bone imaging . . . . . . . . 81
3.6.11 The use of Artificial Intelligence (AI) in
stroke imaging . . . . . . . . . . . . . . . . . . . . . 83
3.6.12 Using AI to treat diseases of the lungs . . . . . . . . 83
3.6.13 Artificial intelligence in the treatment of cancer . . . 83
3.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.8 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . 85
3.9 Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.10 Conflicts of Interest . . . . . . . . . . . . . . . . . . . . . . 85
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4 Patient Monitoring Through Artificial Intelligence 91
Thota Ramathulasi, Rajasekhara Babu, and Mohamed Yousuff
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2 Purpose of Patient Monitoring . . . . . . . . . . . . . . . . 97
4.2.1 Patient monitoring involvement in today’s
healthcare . . . . . . . . . . . . . . . . . . . . . . . 97
4.2.2 Improving healthcare outcomes by using
patient monitoring . . . . . . . . . . . . . . . . . . . 99
4.3 Wearable Patient Monitoring Sensors . . . . . . . . . . . . . 101
4.3.1 Wireless health monitoring specifications . . . . . . 102
4.3.2 Different types of sensors . . . . . . . . . . . . . . . 103
4.4 Involvement of AI in Patient-Monitoring . . . . . . . . . . . 104
4.4.1 Mobility aids the living environment . . . . . . . . . 104
4.4.2 Clinical decision-making assistance . . . . . . . . . 106
4.4.3 Smartphones, apps, sensors, and devices . . . . . . . 108
Contents ix
4.4.4 Processing of text language . . . . . . . . . . . . . . 110
4.4.5 Healthcare applications of text processing
technology . . . . . . . . . . . . . . . . . . . . . . . 110
4.4.6 Using consumer technology to its full potential. . . . 111
4.4.7 AI’s function in diabetes forecasts and
management . . . . . . . . . . . . . . . . . . . . . . 112
4.4.7.1 Apps and technologies for diabetes
monitoring . . . . . . . . . . . . . . . . . . 113
4.5 AI-Assisted Monitoring of the Heart . . . . . . . . . . . . . 114
4.5.1 AI in cardiology with virtual applications . . . . . . . 114
4.5.2 Supporting system in clinical decisions . . . . . . . . 115
4.5.3 Augmented reality (AR), virtual reality (VR), and
virtual assistants . . . . . . . . . . . . . . . . . . . 115
4.5.4 Automated analysis with data . . . . . . . . . . . . . 115
4.6 Neural Applications Linked to AI and Patient Monitoring . . 116
4.6.1 AI for dementia patients . . . . . . . . . . . . . . . . 116
4.6.2 Dementia monitoring . . . . . . . . . . . . . . . . . 117
4.6.3 Supporting dementia patients . . . . . . . . . . . . . 117
4.7 AI for Migraine Patients . . . . . . . . . . . . . . . . . . . . 118
4.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.9 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . 120
4.10 Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
4.11 Conflict of Interest . . . . . . . . . . . . . . . . . . . . . . . 121
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5 Artificial Intelligence: A Promising Approach Toward Targeted
Drug Therapy in Cancer Treatment 129
Amrita Shukla, Simran Ludhiani, Neeraj Kumar,
Shahid Rja, Sudhanshu Mishra, and Subasini Uthirapathy
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 130
5.2 AI, Machine Learning, and Deep Learning . . . . . . . . . . 130
5.3 Drug Development Process . . . . . . . . . . . . . . . . . . 134
5.3.1 Role of AI in chemotherapy . . . . . . . . . . . . . 135
5.3.2 Role of AI in radiotherapy . . . . . . . . . . . . . . . 137
5.3.3 Role of AI in cancer drug development . . . . . . . . 138
5.3.4 Role of AI in immunotherapy . . . . . . . . . . . . . 139
5.4 Monoclonal Antibodies (mAbs) used in
Cancer Treatment . . . . . . . . . . . . . . . . . . . . . . . 140
5.5 MOA of mAbs . . . . . . . . . . . . . . . . . . . . . . . . . 141
5.6 Future Prospects . . . . . . . . . . . . . . . . . . . . . . . 141
