Artificial Intelligence and Cybersecurity in Healthcare
Description
Artificial Intelligence and Cybersecurity in Healthcare provides a crucial exploration of AI and cybersecurity within healthcare Cyber Physical Systems (CPS), offering insights into the complex technological landscape shaping modern patient care and data protection.
As technology advances, healthcare has transformed, particularly through the implementation of CPS that integrate the digital and physical worlds, enhancing system efficiency and effectiveness. This increased reliance on technology raises significant security concerns. The book addresses the integration of AI and cybersecurity in healthcare CPS, detailing technological advancements, applications, and the challenges they present.
AI applications in healthcare CPS include remote patient monitoring, AI chatbots for patient assistance, and biometric authentication for data security. AI not only improves patient care and clinical decision-making by analyzing extensive data and optimizing treatment plans, but also enhances CPS security by detecting and responding to cyber threats. Nonetheless, AI systems are susceptible to attacks, emphasizing the need for robust cybersecurity.
Significant issues include the privacy and security of sensitive healthcare data, potential identity theft, and medical fraud from data breaches, alongside ethical concerns such as algorithmic bias. As the healthcare industry becomes increasingly digital and data-driven, integrating AI and cybersecurity measures into CPS is essential. This requires collaboration among healthcare providers, tech vendors, regulatory bodies, and cybersecurity experts to develop best practices and standards.
This book aims to provide a comprehensive understanding of AI, cybersecurity, and healthcare CPS. It explores technologies like augmented reality, blockchain, and the Internet of Things, addressing associated challenges like cybersecurity threats and ethical dilemmas.
Rashmi Agrawal, PhD, is a professor and the Head of the Department of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India with 20 years of experience in teaching and research. She is a lifetime member of the Computer Society of India, a senior member of the Institute of Electrical and Electronics Engineers, and a chapter chair and professional member of the Association for Computing Machinery. Alongside her affiliations, she is a series editor, has authored and co-authored over 80 research papers in peer-reviewed national and international journals and conferences, and has four patents to her credit as well as a copyright. Additionally, she has contributed as a keynote speaker at IEEE international conferences, an expert lecturer at professional development events, and a session chair for various international conferences.
Pramod Singh Rathore, PhD, is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College and Research Centre, Ajmer, Rajasthan, India and is also visiting faculty at the Government University, MDS Ajmer. He has over eight years of teaching experience and more than 45 publications in peer-reviewed journals, books, and conferences. He has also co-authored and edited numerous books with a variety of global publishers, such as the imprint, Wiley-Scrivener.
Ganesh Gopal Devarajan, PhD, is a professor in the Department of Computer Science and Engineering, SRM Institute of Science and Technology, India with more than 17 years of research and teaching experience in computer science and engineering. He has edited many special issues in reputed journals and is a member of the Institute of Electrical and Electronics Engineers, Association for Computing Machinery, and Computer Society of India. His research interests include Internet of Things (IoT), wireless communication, vehicular communication, and big data.
Rajiva Ranjan Divivedi is an assistant professor in the Computer Science and Engineering Department at SRM Institute of Science and Technology, Delhi, India with over six years of teaching and research experience. He holds a Master's Degree in Computer Science and Engineering and has qualified under both the National Testing Agency's National Eligibility Test and the Graduate Aptitude Test in Engineering. His research interests include machine learning, data analytics, and Internet of Things.
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Preface xix
1 Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology 1
Preeti Narooka and Deepa Parasar
1.1 Introduction 1
1.2 Literature Review 3
1.2.1 Research Paper Survey 3
1.2.2 Existing System Methodologies 5
1.2.3 Comparative Analysis 6
1.2.3.1 Google Cloud Speech-to-Text API 7
1.2.3.2 Microsoft Azure Speech Services 7
1.2.3.3 IBM Watson Speech to Text 7
1.2.3.4 CMU Sphinx 7
1.3 Proposed System 8
1.4 Implementation and Results 11
1.5 Conclusion 14
References 14
2 Securing IoMT-Based Healthcare System: Issues, Challenges, and Solutions 17
Ashok Kumar, Rahul Gupta, Sunil Kumar, Kamlesh Dutta and Mukesh Rani
2.1 Introduction 18
2.1.1 Motivation for the Study 19
2.2 Related Work 20
2.3 SHS Architecture, Applications, and Challenges 23
2.3.1 Applications of the Smart Healthcare System 24
2.3.2 Open Key Challenges 26
2.4 Security Issues in SHS 30
2.5 Security Solutions/Techniques Proposed by Researchers 33
2.6 Future Research Directions 48
2.7 Conclusion 50
References 50
3 Fog Computing in Healthcare: Enhancing Security and Privacy in Distributed Systems 57
Deepa Arora and Oshin Sharma
3.1 Introduction 58
3.1.1 Applications of Fog Computing in Healthcare 61
3.1.2 Technical Details of Implementing Fog Computing in Healthcare System 63
3.2 Case Studies 65
3.2.1 Case Study 1: Remote Monitoring of Patients Using Fog Computing 66
3.2.2 Case Study 2: Fog Computing in Clinical Decision Support 67
3.2.3 Case Study 3: Smart Health 2.0 Project in China 70
3.3 Challenges 73
3.4 Methods to Enhance Security and Privacy in Distributed Systems 74
3.5 Future Directions of Fog Computing in Healthcare 80
3.6 Conclusion 81
References 82
4 Blockchain Technology for Securing Healthcare Data in Cyber-Physical Systems 85
Himanshu Rastogi, Abhay Narayan Tripathi and Bharti Sharma
4.1 What is Healthcare Data? 86
4.1.1 Technologies in Healthcare 88
4.1.1.1 IoT for Healthcare 88
4.1.1.2 Online Healthcare 88
4.1.1.3 Big Data in Healthcare 89
4.1.1.4 Artificial Intelligence in Healthcare 90
4.2 Need of Maintaining Healthcare Data 91
4.3 Risk Associated with Healthcare Data 92
4.4 Cyber-Physical Systems (CPS) 93
4.5 Healthcare Cyber-Physical Systems (HCPS) 97
4.6 Blockchain Technology 99
4.6.1 Block Structure 101
4.6.2 Hashing and Digital Signature 102
4.7 Blockchain Technology in Healthcare Data 103
4.8 Blockchain-Enabled Cyber-Physical Systems (CPS) 106
4.9 Conclusion 108
References 109
5 Augmented Reality and Virtual Reality in Healthcare: Advancements and Security Challenges 113
Srinivas Kumar Palvadi, Pradeep K. G. M., D. Rammurthy, G. Kadiravan and M. M. Prasada Reddy
Introduction 114
Advancements 115
Security Challenges 118
What is Augmented Reality? 123
What is Virtual Reality? 129
Revent Developments in AR and VR 137
Augmented Reality in Ecommerce 138
Virtual Reality in Healthcare 138
Augmented Reality in Advertising 138
Virtual Reality in Education 138
Research Problems in AR and VR in Healthcare 138
User Experience 139
Effectiveness 139
Integration with Clinical Workflow 139
Data Security and Privacy 140
Cost-Effectiveness 140
Challenges in AR and VR in Healthcare 140
Data Privacy and Security 140
Cost 140
Technical Issues 141
Integration with Existing Systems 141
Training and Education 141
Legal and Ethical Considerations 141
Future Research in AR and VR 141
User Experience 142
Health Applications 142
Education and Training 142
Technical Advancements 142
Ethical and Legal Implications 142
Security Challenges in AR and VR 143
Data Privacy 143
Malware and Viruses 143
User Safety 143
Intellectual Property Theft 143
Cybersecurity Vulnerabilities 143
Social Engineering 143
Device and Network Security 144
Conclusion 144
References 144
6 Next Generation Healthcare: Leveraging AI for Personalized Diagnosis, Treatment, and Monitoring 147
Suraj Shukla and Brijesh Kumar
6.1 Introduction 147
6.2 Benefits of AI in Healthcare 149
6.2.1 Personalized Diagnosis and Treatment 149
6.2.2 Improved Diagnostic Accuracy and Speed 150
6.2.3 Accelerated Drug Discovery 151
6.2.4 Remote Monitoring and Early Detection 152
6.3 Challenges of AI in Healthcare 153
6.3.1 Data Privacy and Security 153
6.3.1.1 Data Encryption 154
6.3.1.2 Access Controls 154
6.3.1.3 Data Anonymization 155
6.3.1.4 Secure Infrastructure 155
6.3.1.5 Compliance with Regulations 155
6.3.2 Algorithmic Transparency and Interpretability 155
6.3.2.1 Explainable AI (XAI) Techniques 156
6.3.2.2 Standardized Reporting 156
6.3.2.3 Ethical Considerations 156
6.3.2.4 Regulatory Framework 156
6.3.3 Ethical Considerations 157
6.3.4 Limited Generalizability 159
6.3.5 Regulatory and Legal Frameworks 160
6.3.6 Cyber Threat 161
6.4 Approaches to Addressing Challenges in AI in Healthcare 162
6.4.1 Data Privacy and Security Measures 162
6.4.2 Algorithmic Transparency and Interpretability Techniques 162
6.4.3 Ethical Frameworks and Guidelines 163
6.4.4 Strategies for Enhancing Generalizability 163
6.4.5 Regulatory and Legal Frameworks 163
6.5 Case Studies and Applications of AI in Healthcare 163
6.5.1 Diagnosing Diseases with AI 163
6.5.2 Predictive Analytics for Patient Monitoring 164
6.5.3 Personalized Treatment Recommendations 164
6.5.4 AI-Assisted Robotic Surgery 164
6.5.5 Drug Discovery and Development 164
6.5.5.1 Target Identification and Validation 165
6.5.5.2 Virtual Screening and Drug Design 165
6.5.5.3 Drug Repurposing 165
6.5.5.4 Predictive Toxicology and Safety Assessment 165
6.5.5.5 Clinical Trial Optimization 166
6.5.5.6 Real-Time Monitoring and Surveillance 166
6.5.5.7 Data Integration and Analysis 166
6.5.6 Virtual Assistants and Chatbots 166
6.6 Future Directions and Opportunities in AI for Healthcare 166
6.6.1 Integration of AI with Precision Medicine 167
6.6.2 AI-Powered Drug Discovery and Development 167
6.6.3 Augmented Decision Support Systems 167
6.6.4 Telehealth and Remote Patient Monitoring 168
6.6.5 Explainable AI and Ethical Considerations 168
6.7 Conclusion 168
References 169
7 Exploring the Advantages and Security Aspects of Digital Twin Technology in Healthcare 173
Srinivas Kumar Palvadi, Pradeep K. G. M. and G. Kadiravan
7.1 Introduction 174
7.2 Benefits 176
7.3 Security Considerations 179
7.4 Contribution in this Domain to Healthcare 184
7.5 Medical Device Development 186
7.6 Digital Twin Technology in Healthcare in Future 187
7.7 Continuous UI Upgrades 193
7.7.1 Getting Started with this Domain in Healthcare 193
7.7.2 Future Challenges in the Field 193
7.8 Conclusion 194
References 203
8 An Extensive Study of AI and Cybersecurity in Healthcare 207
Hemlata, Manish Rai and Utsav Krishan Murari
8.1 Introduction 208
8.1.1 Speculating About the Use of AI in Medical Care in the Future 209
8.1.2 Managing the Exchange of Information 211
8.1.3 Considering that Governments Function as Strategic Actors 211
8.1.4 Cybersecurity 213
8.2 Literature Review 213
8.3 Methodology 215
8.4 AI Cybersecurity's Significance for Healthcare 216
8.5 Difficulties with AI Cybersecurity 217
8.6 Conclusion 218
References 218
9 Cloud Computing in Healthcare: Risks and Security Measures 221
Neha Gupta, Rashmi Agrawal and Kavita Arora
Introduction 222
Current State of Healthcare Industry 223
Cloud Computing in Healthcare 225
Benefits of Adopting Cloud in Healthcare 226
Drivers for Cloud Adoption in Healthcare 230
Cloud Challenges in Healthcare 232
Cloud Computing-Based Healthcare Services 235
Current Market Dynamics 237
Impact of Cloud Computing in Indian Healthcare Firms 239
Conclusion 240
References 241
10 Explainable Artificial Intelligence in Healthcare: Transparency and Trustworthiness 243
Sakshi and Gunjan Verma
10.1 Introduction 244
10.1.1 Role of XAI in AI 245
10.1.1.1 Explain to Justify 245
10.1.1.2 Explain to Control 246
10.1.1.3 Explain to Discover 246
10.1.1.4 Explain to Improve 246
10.1.2 Importance of Explainable Artificial Intelligence 247
10.1.2.1 Understanding the Need for Explainability 247
10.1.2.2 Benefits of XAI in Healthcare 248
10.1.3 Addressing the Challenges of XAI Adoption 250
10.1.3.1 Complexity of AI Models 251
10.1.3.2 Trade-Offs Between Accuracy and Interpretability 251
10.1.3.3 Ensuring Generalizability and Robustness 251
10.2 Working of XAI in Healthcare 251
10.2.1 Data Collection 252
10.3 Explorable Artificial Intelligence Techniques and Methods in Healthcare 253
10.3.1 Rule-Based Systems 254
10.3.2 Interpretable Machine Learning Models 254
10.3.3 Visualizations (e.g., Heatmaps) 255
10.3.4 Model-Agnostic Methods (e.g., LIME, SHAP) 255
10.4 Interpretable Deep Learning Models 256
10.4.1 Attention Mechanisms 256
10.4.2 Saliency Maps 257
10.4.3 Concept Activation Vectors 257
10.4.4 Layer-Wise Relevance Propagation 257
10.4.5 Rule Extraction 257
10.4.6 Model Visualization Techniques 258
10.5 Clinical Decision Support System 258
10.6 Explainable Clinical Natural Language Processing 259
10.6.1 Interpretability Techniques for Clinical Text Classification 260
10.6.2 Explaining Named Entity Recognition in Clinical NLP 261
10.6.3 Enhancing Interpretability in Medical Coding 261
10.7 User-Centered Design of XAI Systems 262
10.8 Regulatory and Legal Perspectives in XAI for Healthcare 264
10.8.1 Regulations 265
10.8.2 Legal Framework 265
10.8.3 Data Governance and Privacy Regulations 265
10.8.4 Model Transparency and Accountability 266
10.8.5 Algorithmic Bias and Fairness 266
10.8.6 Explainability and Interpretability 266
10.8.7 Ethical and Legal Responsibility 266
10.9 Ethical Considerations in Explainable Artificial Intelligence (XAI) for Healthcare 267
10.9.1 Bias and Fairness 267
10.9.2 Privacy and Informed Consent 268
10.9.3 Security and Protection Against Adversarial Attacks 268
10.10 Strategies for Promotion of Accountable Use of XAI in Healthcare 268
10.10.1 Explainability and Transparency 269
10.10.2 Human-AI Collaboration and Shared Decision-Making 269
10.10.3 Regulatory Frameworks and Ethical Guidelines 269
10.10.4 Continuous Monitoring and Evaluation 270
Conclusion 270
References 270
11 Fuzzy Expert System to Diagnose the Heart Disease Risk Level 273
B. Lakshmi, K. Sarath, K. Parish Venkata Kumar, G. Praveen, B. Karthik and Y. Phani Bhushan
11.1 Introduction 274
11.2 Work Related 275
11.3 Expert Methods for Medical Diagnosis 276
11.4 Parameter Input 277
11.4.1 Cholesterol 277
11.4.2 Blood Pressure (BP) 278
11.4.3 Sugar Blood 278
11.4.4 Rate of Heart 279
11.4.5 Glucose Meter 279
11.4.6 Monitor Blood Pressure 279
11.5 System Flow 279
11.5.1 Input and Output of Fuzzy 280
11.5.2 System Workflow Based on Fuzzy 280
11.5.3 Data Set 280
11.6 Simulation and Result 281
11.6.1 Accuracy Level of Expert System 284
11.7 Conclusion 285
References 285
12 Search and Rescue-Based Sparse Auto-Encoder for Detecting Heart Disease in IoT Healthcare Environment 289
Rakesh Chandrashekar, B. Gunapriya and Balasubramanian Prabhu Kavin
12.1 Introduction 290
12.2 Related Works 291
12.3 Proposed Model 294
12.3.1 Dataset Description 294
12.3.2 Pre-Processing 294
12.3.3 Feature Selection Using Artificial Fish Swarm Optimization (AFO) 296
12.3.3.1 Prey Behavior 296
12.3.3.2 Swarm Behavior 296
12.3.3.3 Follow Behavior 297
12.3.4 Prediction of Heart Disease Using ISAE Model 297
12.3.4.1 Design of the SRO Algorithm 298
12.4 Results and Discussion 301
12.4.1 An Experimental Setup Details 301
12.4.2 Experiment System Characteristics 302
12.4.3 Performance Metrics 302
12.5 Conclusion and Future Work 306
References 307
13 Growth Optimization-Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment 311
Jayasheel Kumar Kalagatoori Archakam, Santosh Kumar B. and Balasubramanian Prabhu Kavin
13.1 Introduction 312
13.2 Related Works 313
13.2.1 Challenges 315
13.3 Proposed Model 315
13.3.1 Overall IoMT-Based Basis 315
13.3.2 Proposed Methodology 316
13.3.2.1 Stacked Bidirectional LSTM RNN for Disease Prediction 317
13.3.2.2 Growth Optimizer 318
13.4 Results and Discussion 320
13.4.1 Dataset 321
13.4.1.1 Wisconsin Breast Cancer Dataset 321
13.4.2 Model Assessment 321
13.5 Conclusion 325
References 326
14 Lightweight Fuzzy Logical MQTT Security System to Secure the Low Configurated Medical Device System by Communicating the IoT 329
Basi Reddy A., Kanegonda Ravi Chythanya, Sharada K. A. and R. Senthamil Selvan
14.1 Introduction 330
14.2 Methodology of FLS 331
14.3 Problem Identification 332
14.3.1 Framework 332
14.3.1.1 Threat Modelling 333
14.3.1.2 Attack Outline 333
14.3.1.3 Design Idea 333
14.4 Proposed Approach 334
14.5 Result with Discussion 335
14.5.1 Intrusion Detection System Analysis Metrics 336
14.5.1.1 Threat Detection Efficiency 336
14.5.1.2 Threat Detection Rate 336
14.5.1.3 Threat Detection Accuracy (TDA) Ratio 340
14.5.1.4 False vs. Positive Rate (FPR) 340
14.5.2 Communication Rate 340
14.5.2.1 Precision 342
14.5.2.2 Recall 342
14.5.2.3 F-Score 342
14.6 Conclusion 344
References 345
15 IoT-Based Secured Biomedical Device to Remote Monitoring to the Patient 349
Dinesh G., Jeevanarao Batakala, Yousef A. Baker El-Ebiary and N. Ashokkumar
15.1 Introduction 350
15.2 Internet of Things 353
15.3 IoMT 354
15.3.1 Real Application of IoT 354
15.3.2 Ransomware 355
15.3.2.1 Target and Ransomware Implications 356
15.3.2.2 How Ransomware Works 356
15.4 Biostatistical Techniques for Maintaining Security Goals 356
15.5 Healthcare IT System Through Biometric BioMT Approach 357
15.6 Conclusion 359
References 360
16 Fuzzy Interface Drug Delivery Decision-Making Algorithm 365
Yogendra Narayan, Mukta Sandhu, Yousef A. Baker El-Ebiary and N. Ashokkumar
16.1 Introduction 366
16.2 Description and Problems 367
16.3 Methods 367
16.3.1 Tree Decision 369
16.3.2 Fuzzy Inference System 370
16.3.3 Fuzzification of Decision Rules of Tree 370
16.3.4 FIS Decision Making 371
16.4 Application of Analgesia 373
16.4.1 Analgesia Nociception Index 373
16.4.2 Data Collection/Preprocessing 373
16.5 Result 374
16.5.1 FIS of Structure 374
16.6 Discussion 376
16.7 Conclusion 377
References 377
17 Implementation of Clinical Fuzzy-Based Decision Supportive System to Monitor Renal Function 381
S. Dinesh Kumar, M. J. D. Ebinezer and N. Ashokkumar
17.1 Introduction 382
17.1.1 Expert Systems of FIS 383
17.1.2 Neuro Adaptive of FIS 384
17.1.2.1 Fuzzification Layer, First Layer 385
17.1.2.2 Law Layer, Second Layer 385
17.1.2.3 Normalization Layer, Fourth Layer 385
17.1.2.4 Defuzzification 385
17.1.2.5 The Summation Layer, or Fifth Layer 385
17.2 Work Related 386
17.3 Methods 387
17.3.1 MATLAB 391
17.4 Discussion and Results 392
17.5 Conclusion 393
References 393
18 Deep Learning-Based Medical Image Classification and Web Application Framework to Identify Alzheimer's Disease 397
K. Parish Venkata Kumar, Piyush Charan, S. Kayalvili and M. V. B. T. Santhi
18.1 Introduction 398
18.2 Proposed Methodology 401
18.2.1 Various Techniques Used 402
18.3 Experiment Setup 404
18.4 Result 405
18.5 Discussion of Result 408
18.6 Conclusion 409
References 410
19 Using Deep Learning to Classify and Diagnose Alzheimer's Disease 413
A. V. Sriharsha
19.1 Introduction 413
19.2 Biomarkers and Detection of Alzheimer's Disease 414
19.2.1 AD Biomarkers 414
19.2.2 Data Preprocessing 415
19.2.3 Management of Data 416
19.2.4 Patch Based 416
19.3 Methods 417
19.3.1 The E 2 AD 2 C Framework 417
19.3.2 Data Normalization 420
19.3.3 Methods and Technique 420
19.4 Model Evaluation and Methods 422
19.4.1 Checking the Web Services 423
19.4.2 Other Fuzzy Systems of Diagnosis of Diseases 424
19.5 Conclusion 425
References 425
20 Developing a Soft Computing Fuzzy Interface System for Peptic Ulcer Diagnosis 429
B. Lakshmi, K. Parish Venkata Kumar and N. Ashokkumar
20.1 Introduction 430
20.2 Methodology 431
20.2.1 Animals 431
20.2.2 Method Chemical of Gastric Ulcer 432
20.2.3 Index Measurement of Ulcer 432
20.2.4 Data Sets 432
20.2.5 Fuzzy Expert System 433
20.3 Results 434
20.3.1 Variables of Input and Output 434
20.3.2 Methods 435
20.3.3 EOC Analysis 437
20.3.4 Other Fuzzy Expert Systems for Disease Diagnosis 438
20.4 Conclusion 439
References 440
21 Digital Twin Technology in Healthcare: Benefits and Security Considerations 443
Priyanka Tyagi and Kajol Mittal
Introduction 444
Conclusion 457
References 458
22 Combating Cyber Threats Including Wormhole Attacks in Healthcare Cyber-Physical Systems: Advanced Prevention and Mitigation Techniques 461
Pramod Singh Rathore and Mrinal Kanti Sarkar
22.1 Introduction to Cybersecurity in Healthcare Cyber-Physical Systems 462
22.2 Understanding Cyber Threats in Healthcare 463
22.2.1 Types of Cyber Threats in Healthcare Systems 463
22.2.2 Special Focus on Wormhole Attacks 464
22.2.3 Case Studies: Recent Cyberattacks in Healthcare 464
22.3 Vulnerabilities in Healthcare Cyber-Physical Systems 465
22.3.1 Identifying Common Vulnerabilities 465
22.3.2 Impact of Wormhole Attacks on Healthcare Systems 466
22.3.3 Assessing Risks in Connected Medical Devices 466
22.4 Advanced Prevention Techniques 466
22.4.1 Implementing Robust Encryption Protocols 467
22.4.2 Role of Firewalls and Intrusion Detection Systems 467
22.4.3 Preventive Measures for Wormhole Attacks 467
22.5 Mitigation Strategies for Cyber Threats 468
22.5.1 Developing an Effective Incident Response Plan 468
22.5.2 Strategies for Containing and Mitigating Wormhole Attacks 469
22.5.3 Disaster Recovery and Business Continuity Planning 469
22.6 Emerging Technologies and Future Trends 469
22.6.1 The Role of Artificial Intelligence in Cybersecurity 470
22.6.2 Blockchain for Secure Healthcare Data Management 470
22.6.3 Future Challenges and Opportunities in Healthcare Cybersecurity 470
22.7 Training and Awareness Programs 471
22.7.1 Educating Healthcare Staff on Cybersecurity Best Practices 471
22.7.2 Training Programs for Wormhole Attack Prevention 471
References 472
Index 475
Preface
In the era of digital transformation, healthcare stands at the confluence of immense possibilities and complex challenges. The promise of better patient care, more accurate diagnostics, and personalized treatment is being realized daily. Yet, as with all revolutions, there are new challenges to face. Artificial Intelligence and Cybersecurity in Healthcare delves into the intricate dance between the vast potentials of artificial intelligence (AI) and the imperatives of cyber security in the healthcare industry.
The intersection of AI and cyber-physical systems in healthcare, from smart hospital rooms to wearable diagnostics, is reshaping the way we think about medical intervention and patient care. Such advancements are not just incremental; they have the potential to redefine the very paradigms of healthcare delivery. However, the introduction of these technologies also means that healthcare systems are more vulnerable to cyber threats, with potentially life-threatening consequences.
This book is a clarion call to researchers, practitioners, and enthusiasts alike. It outlines not only the myriad opportunities presented by AI in healthcare but also the urgent need for robust and proactive cybersecurity measures. Each chapter unravels a different dimension of this multidisciplinary field, drawing on real-world case studies, cutting-edge research, and expert opinions.
As you turn the pages, you'll be invited to envision a future where AI-driven healthcare cyber-physical systems are both groundbreaking and secure. A future where technology augments human capabilities, rather than replacing or endangering them. This book serves as both a comprehensive guide and a challenge: to harness the power of AI for healthcare, while ensuring the utmost safety and security for patients.
In our pursuit of better health and well-being, it is essential to understand the balance of innovation and security. Artificial Intelligence and Cybersecurity in Healthcare is your roadmap to this brave new world.
Organization of the Book
This book is organized into twenty chapters. In Chapter 1, this Chapter discusses about the the study is based on machine learning and statistical models, were applied to develop a speech recognition system. As a result of the system, it can convert speech to text that can then be benefited for a variety of purposes, including voice commands, transcription services, and speech-to-text functions. Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs) are combined in the proposed system to enhance existing acoustic modelling techniques. Additionally, the report compares various existing approaches, identifies their flaws, and suggests ways to improve them. The proposed system is implemented and assessed using a publicly accessible dataset, and the findings are discussed.
In Chapter 2, Smart healthcare systems utilise wireless networks and information technology to facilitate the interchange and analysis of patient data. If the security and control measures of the smart healthcare system are insufficient, it becomes vulnerable to compromise by attackers. This vulnerability presents an opportunity for attackers to inflict harm upon patients, potentially leading to fatal consequences, all while remaining undetected. The intrinsic attributes of intelligent healthcare systems, such as their capacity for expansion, intricate nature, and diverse range of devices, pose significant challenges in promptly identifying and safeguarding against such cyber threats. This chapter endeavours to offer a methodical and all-encompassing examination of the security and privacy concerns linked to Smart Home Systems (SHS), as well as the security solutions put forth by the research community to safeguard SHS. Ultimately, the chapter culminates by presenting many prospective avenues for future research within the realm of safeguarding Internet of Medical Things (IoMT)-based intelligent healthcare systems.
In Chapter 3, this chapter explores the use of fog computing in healthcare along with enhancement of security and privacy in distributed systems. We provide an overview of the key concepts and architectures of fog computing and discuss the unique security and privacy challenges that arise in healthcare. We then review existing solutions and techniques for enhancing security and privacy in fog computing-based healthcare systems, including data encryption, access control, and privacy-preserving data analysis. Finally, we highlight some of the open research challenges and opportunities in this area, and provide recommendations for future research directions.
In Chapter 4, in this research chapter, users offers the capability of remote monitoring and management of physical systems, which may save time and money. But technology also brings along other difficulties, such interoperability, security, and privacy. Healthcare cyber-physical data has the ability to transform the healthcare sector, but for it to do so safely and effectively, it has to be carefully planned, managed, and secured. A significant quantity of data produced by healthcare cyber physical systems may be utilized to enhance patient care and guide healthcare policies. However, in order to safeguard this private information and keep patients' confidence, healthcare organizations must likewise place a high priority on cyber-security.
In Chapter 5, the book chapter affords brief and general information regarding AR & VR technology over health domain, consisting of the blessings as well as capacity packages which additionally discuss regarding safety problems which were merged while taking the concept of AR & VR over health domain which brings the ability solutions to mitigate those challenges. Universal, advantages of AR & VR over health sector affords giant possibilities in developing patient effects, improving scientific training and study as well as allowing remote collaboration along telemedicine. But, addressing the safety demanding situations related to bringing those domains were crucial for making certain secure along best use over health domain meaningful, all models were extended by adding 3 layers at the end to improve their performance. The performance of the VGG19 model was found to be better and was able to classify almost all images belonging to 21 classes with an accuracy of 100% in training and 95.07% in testing data, followed by VGG16 with 93% and ResNet with 91% accuracy in testing data.
In Chapter 6, AI algorithms can analyze large volumes of patient data to create personalized treatment plans that consider individual medical history, genetics, and lifestyle factors. AI can also improve the accuracy and speed of diagnoses, as well as accelerate drug discovery. Remote monitoring and care can be facilitated by IoT devices, with AI analysis allowing for early detection of health issues. While AI holds tremendous potential for healthcare, data privacy, and security remain critical concerns, as does the need for transparency and accountability in the design and deployment of AI algorithms. This paper examines the benefits and challenges of AI in healthcare and demonstrates how it can improve patient outcomes and healthcare deliverys.
In Chapter 7, Computerized mechanism gives the true entity, such as structure, that helps us understand how well our systems are functioning and enables us to make better decisions regarding necessary improvements This ensures that there is always sufficient oxygen available for emergency transfusions. Nonetheless, there are several challenges to address before implementing digital twins in healthcare. Firstly, it is crucial to find a twin that closely matches the age, health, and other characteristics of the system being monitored. Additionally, the genetic profiles of the twins must be comparable to ensure the accuracy of the data. Lastly, both parties involved need to agree on the shared use of the twin's information. Failure to address these considerations could lead to disregard of the dual usage of the twin technique by either party or clients.
In Chapter 8, we provide an analytical structure for examining these sociotechnical imaginaries, emphasising three key aspects: (a) healthcare and AI imaginaries; (b) their performativity; and (c) the socio-governmental background in which they are expressed. We determine three strategies for envisioning the foreseeable future of AI and medical treatments, namely strategies of 1) authorization, 2) advertisement, and 3) a sense of security. and Supported by an indispensable multimodal examination of the discourse of the regulatory initiative "Valuable AI," these strategies add to the debate over policy regarding how to organise health care information in the age of artificial intelligence and how to encourage patients to make available their health information. Current methods of exchanging data limit the amount of personal information that may be shared. However, since healthcare AI systems depend on data to expand their capabilities, this kind of data deficiency makes it more difficult to create potential uses and reduces the amount of data required to support them. Three metrics in supply chains-resilience, long-term viability, and cyber-security-define how reliably they function without interruption.
In Chapter 9, Cloud computing is becoming increasingly popular in the healthcare sector, notably in the months following the COVID-19 outbreak. According to www.businesswire.com, the global computer industry in the healthcare sector will be worth $25.54 billion in 2024 and $89 billion in 2027...
