Artificial Intelligence and Cybersecurity in Healthcare

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.

• Preface xix1 Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology 1Preeti Narooka and Deepa Parasar1.1 Introduction 11.2 Literature Review 31.2.1 Research Paper Survey 31.2.2 Existing System Methodologies 51.2.3 Comparative Analysis 61.2.3.1 Google Cloud Speech-to-Text API 71.2.3.2 Microsoft Azure Speech Services 71.2.3.3 IBM Watson Speech to Text 71.2.3.4 CMU Sphinx 71.3 Proposed System 81.4 Implementation and Results 111.5 Conclusion 14References 142 Securing IoMT-Based Healthcare System: Issues, Challenges, and Solutions 17Ashok Kumar, Rahul Gupta, Sunil Kumar, Kamlesh Dutta and Mukesh Rani2.1 Introduction 182.1.1 Motivation for the Study 192.2 Related Work 202.3 SHS Architecture, Applications, and Challenges 232.3.1 Applications of the Smart Healthcare System 242.3.2 Open Key Challenges 262.4 Security Issues in SHS 302.5 Security Solutions/Techniques Proposed by Researchers 332.6 Future Research Directions 482.7 Conclusion 50References 503 Fog Computing in Healthcare: Enhancing Security and Privacy in Distributed Systems 57Deepa Arora and Oshin Sharma3.1 Introduction 583.1.1 Applications of Fog Computing in Healthcare 613.1.2 Technical Details of Implementing Fog Computing in Healthcare System 633.2 Case Studies 653.2.1 Case Study 1: Remote Monitoring of Patients Using Fog Computing 663.2.2 Case Study 2: Fog Computing in Clinical Decision Support 673.2.3 Case Study 3: Smart Health 2.0 Project in China 703.3 Challenges 733.4 Methods to Enhance Security and Privacy in Distributed Systems 743.5 Future Directions of Fog Computing in Healthcare 803.6 Conclusion 81References 824 Blockchain Technology for Securing Healthcare Data in Cyber-Physical Systems 85Himanshu Rastogi, Abhay Narayan Tripathi and Bharti Sharma4.1 What is Healthcare Data? 864.1.1 Technologies in Healthcare 884.1.1.1 IoT for Healthcare 884.1.1.2 Online Healthcare 884.1.1.3 Big Data in Healthcare 894.1.1.4 Artificial Intelligence in Healthcare 904.2 Need of Maintaining Healthcare Data 914.3 Risk Associated with Healthcare Data 924.4 Cyber-Physical Systems (CPS) 934.5 Healthcare Cyber-Physical Systems (HCPS) 974.6 Blockchain Technology 994.6.1 Block Structure 1014.6.2 Hashing and Digital Signature 1024.7 Blockchain Technology in Healthcare Data 1034.8 Blockchain-Enabled Cyber-Physical Systems (CPS) 1064.9 Conclusion 108References 1095 Augmented Reality and Virtual Reality in Healthcare: Advancements and Security Challenges 113Srinivas Kumar Palvadi, Pradeep K. G. M., D. Rammurthy, G. Kadiravan and M. M. Prasada ReddyIntroduction 114Advancements 115Security Challenges 118What is Augmented Reality? 123What is Virtual Reality? 129Revent Developments in AR and VR 137Augmented Reality in Ecommerce 138Virtual Reality in Healthcare 138Augmented Reality in Advertising 138Virtual Reality in Education 138Research Problems in AR and VR in Healthcare 138User Experience 139Effectiveness 139Integration with Clinical Workflow 139Data Security and Privacy 140Cost-Effectiveness 140Challenges in AR and VR in Healthcare 140Data Privacy and Security 140Cost 140Technical Issues 141Integration with Existing Systems 141Training and Education 141Legal and Ethical Considerations 141Future Research in AR and VR 141User Experience 142Health Applications 142Education and Training 142Technical Advancements 142Ethical and Legal Implications 142Security Challenges in AR and VR 143Data Privacy 143Malware and Viruses 143User Safety 143Intellectual Property Theft 143Cybersecurity Vulnerabilities 143Social Engineering 143Device and Network Security 144Conclusion 144References 1446 Next Generation Healthcare: Leveraging AI for Personalized Diagnosis, Treatment, and Monitoring 147Suraj Shukla and Brijesh Kumar6.1 Introduction 1476.2 Benefits of AI in Healthcare 1496.2.1 Personalized Diagnosis and Treatment 1496.2.2 Improved Diagnostic Accuracy and Speed 1506.2.3 Accelerated Drug Discovery 1516.2.4 Remote Monitoring and Early Detection 1526.3 Challenges of AI in Healthcare 1536.3.1 Data Privacy and Security 1536.3.1.1 Data Encryption 1546.3.1.2 Access Controls 1546.3.1.3 Data Anonymization 1556.3.1.4 Secure Infrastructure 1556.3.1.5 Compliance with Regulations 1556.3.2 Algorithmic Transparency and Interpretability 1556.3.2.1 Explainable AI (XAI) Techniques 1566.3.2.2 Standardized Reporting 1566.3.2.3 Ethical Considerations 1566.3.2.4 Regulatory Framework 1566.3.3 Ethical Considerations 1576.3.4 Limited Generalizability 1596.3.5 Regulatory and Legal Frameworks 1606.3.6 Cyber Threat 1616.4 Approaches to Addressing Challenges in AI in Healthcare 1626.4.1 Data Privacy and Security Measures 1626.4.2 Algorithmic Transparency and Interpretability Techniques 1626.4.3 Ethical Frameworks and Guidelines 1636.4.4 Strategies for Enhancing Generalizability 1636.4.5 Regulatory and Legal Frameworks 1636.5 Case Studies and Applications of AI in Healthcare 1636.5.1 Diagnosing Diseases with AI 1636.5.2 Predictive Analytics for Patient Monitoring 1646.5.3 Personalized Treatment Recommendations 1646.5.4 AI-Assisted Robotic Surgery 1646.5.5 Drug Discovery and Development 1646.5.5.1 Target Identification and Validation 1656.5.5.2 Virtual Screening and Drug Design 1656.5.5.3 Drug Repurposing 1656.5.5.4 Predictive Toxicology and Safety Assessment 1656.5.5.5 Clinical Trial Optimization 1666.5.5.6 Real-Time Monitoring and Surveillance 1666.5.5.7 Data Integration and Analysis 1666.5.6 Virtual Assistants and Chatbots 1666.6 Future Directions and Opportunities in AI for Healthcare 1666.6.1 Integration of AI with Precision Medicine 1676.6.2 AI-Powered Drug Discovery and Development 1676.6.3 Augmented Decision Support Systems 1676.6.4 Telehealth and Remote Patient Monitoring 1686.6.5 Explainable AI and Ethical Considerations 1686.7 Conclusion 168References 1697 Exploring the Advantages and Security Aspects of Digital Twin Technology in Healthcare 173Srinivas Kumar Palvadi, Pradeep K. G. M. and G. Kadiravan7.1 Introduction 1747.2 Benefits 1767.3 Security Considerations 1797.4 Contribution in this Domain to Healthcare 1847.5 Medical Device Development 1867.6 Digital Twin Technology in Healthcare in Future 1877.7 Continuous UI Upgrades 1937.7.1 Getting Started with this Domain in Healthcare 1937.7.2 Future Challenges in the Field 1937.8 Conclusion 194References 2038 An Extensive Study of AI and Cybersecurity in Healthcare 207Hemlata, Manish Rai and Utsav Krishan Murari8.1 Introduction 2088.1.1 Speculating About the Use of AI in Medical Care in the Future 2098.1.2 Managing the Exchange of Information 2118.1.3 Considering that Governments Function as Strategic Actors 2118.1.4 Cybersecurity 2138.2 Literature Review 2138.3 Methodology 2158.4 AI Cybersecurity’s Significance for Healthcare 2168.5 Difficulties with AI Cybersecurity 2178.6 Conclusion 218References 2189 Cloud Computing in Healthcare: Risks and Security Measures 221Neha Gupta, Rashmi Agrawal and Kavita AroraIntroduction 222Current State of Healthcare Industry 223Cloud Computing in Healthcare 225Benefits of Adopting Cloud in Healthcare 226Drivers for Cloud Adoption in Healthcare 230Cloud Challenges in Healthcare 232Cloud Computing–Based Healthcare Services 235Current Market Dynamics 237Impact of Cloud Computing in Indian Healthcare Firms 239Conclusion 240References 24110 Explainable Artificial Intelligence in Healthcare: Transparency and Trustworthiness 243Sakshi and Gunjan Verma10.1 Introduction 24410.1.1 Role of XAI in AI 24510.1.1.1 Explain to Justify 24510.1.1.2 Explain to Control 24610.1.1.3 Explain to Discover 24610.1.1.4 Explain to Improve 24610.1.2 Importance of Explainable Artificial Intelligence 24710.1.2.1 Understanding the Need for Explainability 24710.1.2.2 Benefits of XAI in Healthcare 24810.1.3 Addressing the Challenges of XAI Adoption 25010.1.3.1 Complexity of AI Models 25110.1.3.2 Trade-Offs Between Accuracy and Interpretability 25110.1.3.3 Ensuring Generalizability and Robustness 25110.2 Working of XAI in Healthcare 25110.2.1 Data Collection 25210.3 Explorable Artificial Intelligence Techniques and Methods in Healthcare 25310.3.1 Rule-Based Systems 25410.3.2 Interpretable Machine Learning Models 25410.3.3 Visualizations (e.g., Heatmaps) 25510.3.4 Model-Agnostic Methods (e.g., LIME, SHAP) 25510.4 Interpretable Deep Learning Models 25610.4.1 Attention Mechanisms 25610.4.2 Saliency Maps 25710.4.3 Concept Activation Vectors 25710.4.4 Layer-Wise Relevance Propagation 25710.4.5 Rule Extraction 25710.4.6 Model Visualization Techniques 25810.5 Clinical Decision Support System 25810.6 Explainable Clinical Natural Language Processing 25910.6.1 Interpretability Techniques for Clinical Text Classification 26010.6.2 Explaining Named Entity Recognition in Clinical NLP 26110.6.3 Enhancing Interpretability in Medical Coding 26110.7 User-Centered Design of XAI Systems 26210.8 Regulatory and Legal Perspectives in XAI for Healthcare 26410.8.1 Regulations 26510.8.2 Legal Framework 26510.8.3 Data Governance and Privacy Regulations 26510.8.4 Model Transparency and Accountability 26610.8.5 Algorithmic Bias and Fairness 26610.8.6 Explainability and Interpretability 26610.8.7 Ethical and Legal Responsibility 26610.9 Ethical Considerations in Explainable Artificial Intelligence (XAI) for Healthcare 26710.9.1 Bias and Fairness 26710.9.2 Privacy and Informed Consent 26810.9.3 Security and Protection Against Adversarial Attacks 26810.10 Strategies for Promotion of Accountable Use of XAI in Healthcare 26810.10.1 Explainability and Transparency 26910.10.2 Human-AI Collaboration and Shared Decision-Making 26910.10.3 Regulatory Frameworks and Ethical Guidelines 26910.10.4 Continuous Monitoring and Evaluation 270Conclusion 270References 27011 Fuzzy Expert System to Diagnose the Heart Disease Risk Level 273B. Lakshmi, K. Sarath, K. Parish Venkata Kumar, G. Praveen, B. Karthik and Y. Phani Bhushan11.1 Introduction 27411.2 Work Related 27511.3 Expert Methods for Medical Diagnosis 27611.4 Parameter Input 27711.4.1 Cholesterol 27711.4.2 Blood Pressure (BP) 27811.4.3 Sugar Blood 27811.4.4 Rate of Heart 27911.4.5 Glucose Meter 27911.4.6 Monitor Blood Pressure 27911.5 System Flow 27911.5.1 Input and Output of Fuzzy 28011.5.2 System Workflow Based on Fuzzy 28011.5.3 Data Set 28011.6 Simulation and Result 28111.6.1 Accuracy Level of Expert System 28411.7 Conclusion 285References 28512 Search and Rescue–Based Sparse Auto‐Encoder for Detecting Heart Disease in IoT Healthcare Environment 289Rakesh Chandrashekar, B. Gunapriya and Balasubramanian Prabhu Kavin12.1 Introduction 29012.2 Related Works 29112.3 Proposed Model 29412.3.1 Dataset Description 29412.3.2 Pre-Processing 29412.3.3 Feature Selection Using Artificial Fish Swarm Optimization (AFO) 29612.3.3.1 Prey Behavior 29612.3.3.2 Swarm Behavior 29612.3.3.3 Follow Behavior 29712.3.4 Prediction of Heart Disease Using ISAE Model 29712.3.4.1 Design of the SRO Algorithm 29812.4 Results and Discussion 30112.4.1 An Experimental Setup Details 30112.4.2 Experiment System Characteristics 30212.4.3 Performance Metrics 30212.5 Conclusion and Future Work 306References 30713 Growth Optimization–Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment 311Jayasheel Kumar Kalagatoori Archakam, Santosh Kumar B. and Balasubramanian Prabhu Kavin13.1 Introduction 31213.2 Related Works 31313.2.1 Challenges 31513.3 Proposed Model 31513.3.1 Overall IoMT-Based Basis 31513.3.2 Proposed Methodology 31613.3.2.1 Stacked Bidirectional LSTM RNN for Disease Prediction 31713.3.2.2 Growth Optimizer 31813.4 Results and Discussion 32013.4.1 Dataset 32113.4.1.1 Wisconsin Breast Cancer Dataset 32113.4.2 Model Assessment 32113.5 Conclusion 325References 32614 Lightweight Fuzzy Logical MQTT Security System to Secure the Low Configurated Medical Device System by Communicating the IoT 329Basi Reddy A., Kanegonda Ravi Chythanya, Sharada K. A. and R. Senthamil Selvan14.1 Introduction 33014.2 Methodology of FLS 33114.3 Problem Identification 33214.3.1 Framework 33214.3.1.1 Threat Modelling 33314.3.1.2 Attack Outline 33314.3.1.3 Design Idea 33314.4 Proposed Approach 33414.5 Result with Discussion 33514.5.1 Intrusion Detection System Analysis Metrics 33614.5.1.1 Threat Detection Efficiency 33614.5.1.2 Threat Detection Rate 33614.5.1.3 Threat Detection Accuracy (TDA) Ratio 34014.5.1.4 False vs. Positive Rate (FPR) 34014.5.2 Communication Rate 34014.5.2.1 Precision 34214.5.2.2 Recall 34214.5.2.3 F-Score 34214.6 Conclusion 344References 34515 IoT-Based Secured Biomedical Device to Remote Monitoring to the Patient 349Dinesh G., Jeevanarao Batakala, Yousef A. Baker El-Ebiary and N. Ashokkumar15.1 Introduction 35015.2 Internet of Things 35315.3 IoMT 35415.3.1 Real Application of IoT 35415.3.2 Ransomware 35515.3.2.1 Target and Ransomware Implications 35615.3.2.2 How Ransomware Works 35615.4 Biostatistical Techniques for Maintaining Security Goals 35615.5 Healthcare IT System Through Biometric BioMT Approach 35715.6 Conclusion 359References 36016 Fuzzy Interface Drug Delivery Decision-Making Algorithm 365Yogendra Narayan, Mukta Sandhu, Yousef A. Baker El-Ebiary and N. Ashokkumar16.1 Introduction 36616.2 Description and Problems 36716.3 Methods 36716.3.1 Tree Decision 36916.3.2 Fuzzy Inference System 37016.3.3 Fuzzification of Decision Rules of Tree 37016.3.4 FIS Decision Making 37116.4 Application of Analgesia 37316.4.1 Analgesia Nociception Index 37316.4.2 Data Collection/Preprocessing 37316.5 Result 37416.5.1 FIS of Structure 37416.6 Discussion 37616.7 Conclusion 377References 37717 Implementation of Clinical Fuzzy‐Based Decision Supportive System to Monitor Renal Function 381S. Dinesh Kumar, M. J. D. Ebinezer and N. Ashokkumar17.1 Introduction 38217.1.1 Expert Systems of FIS 38317.1.2 Neuro Adaptive of FIS 38417.1.2.1 Fuzzification Layer, First Layer 38517.1.2.2 Law Layer, Second Layer 38517.1.2.3 Normalization Layer, Fourth Layer 38517.1.2.4 Defuzzification 38517.1.2.5 The Summation Layer, or Fifth Layer 38517.2 Work Related 38617.3 Methods 38717.3.1 MATLAB 39117.4 Discussion and Results 39217.5 Conclusion 393References 39318 Deep Learning–Based Medical Image Classification and Web Application Framework to Identify Alzheimer’s Disease 397K. Parish Venkata Kumar, Piyush Charan, S. Kayalvili and M. V. B. T. Santhi18.1 Introduction 39818.2 Proposed Methodology 40118.2.1 Various Techniques Used 40218.3 Experiment Setup 40418.4 Result 40518.5 Discussion of Result 40818.6 Conclusion 409References 41019 Using Deep Learning to Classify and Diagnose Alzheimer’s Disease 413A. V. Sriharsha19.1 Introduction 41319.2 Biomarkers and Detection of Alzheimer’s Disease 41419.2.1 AD Biomarkers 41419.2.2 Data Preprocessing 41519.2.3 Management of Data 41619.2.4 Patch Based 41619.3 Methods 41719.3.1 The E 2 AD 2 C Framework 41719.3.2 Data Normalization 42019.3.3 Methods and Technique 42019.4 Model Evaluation and Methods 42219.4.1 Checking the Web Services 42319.4.2 Other Fuzzy Systems of Diagnosis of Diseases 42419.5 Conclusion 425References 42520 Developing a Soft Computing Fuzzy Interface System for Peptic Ulcer Diagnosis 429B. Lakshmi, K. Parish Venkata Kumar and N. Ashokkumar20.1 Introduction 43020.2 Methodology 43120.2.1 Animals 43120.2.2 Method Chemical of Gastric Ulcer 43220.2.3 Index Measurement of Ulcer 43220.2.4 Data Sets 43220.2.5 Fuzzy Expert System 43320.3 Results 43420.3.1 Variables of Input and Output 43420.3.2 Methods 43520.3.3 EOC Analysis 43720.3.4 Other Fuzzy Expert Systems for Disease Diagnosis 43820.4 Conclusion 439References 44021 Digital Twin Technology in Healthcare: Benefits and Security Considerations 443Priyanka Tyagi and Kajol MittalIntroduction 444Conclusion 457References 45822 Combating Cyber Threats Including Wormhole Attacks in Healthcare Cyber-Physical Systems: Advanced Prevention and Mitigation Techniques 461Pramod Singh Rathore and Mrinal Kanti Sarkar22.1 Introduction to Cybersecurity in Healthcare Cyber-Physical Systems 46222.2 Understanding Cyber Threats in Healthcare 46322.2.1 Types of Cyber Threats in Healthcare Systems 46322.2.2 Special Focus on Wormhole Attacks 46422.2.3 Case Studies: Recent Cyberattacks in Healthcare 46422.3 Vulnerabilities in Healthcare Cyber-Physical Systems 46522.3.1 Identifying Common Vulnerabilities 46522.3.2 Impact of Wormhole Attacks on Healthcare Systems 46622.3.3 Assessing Risks in Connected Medical Devices 46622.4 Advanced Prevention Techniques 46622.4.1 Implementing Robust Encryption Protocols 46722.4.2 Role of Firewalls and Intrusion Detection Systems 46722.4.3 Preventive Measures for Wormhole Attacks 46722.5 Mitigation Strategies for Cyber Threats 46822.5.1 Developing an Effective Incident Response Plan 46822.5.2 Strategies for Containing and Mitigating Wormhole Attacks 46922.5.3 Disaster Recovery and Business Continuity Planning 46922.6 Emerging Technologies and Future Trends 46922.6.1 The Role of Artificial Intelligence in Cybersecurity 47022.6.2 Blockchain for Secure Healthcare Data Management 47022.6.3 Future Challenges and Opportunities in Healthcare Cybersecurity 47022.7 Training and Awareness Programs 47122.7.1 Educating Healthcare Staff on Cybersecurity Best Practices 47122.7.2 Training Programs for Wormhole Attack Prevention 471References 472Index 475