{"product_id":"artificial-intelligence-and-cybersecurity-in-healthcare-hardback-9781394229796","title":"Artificial Intelligence and Cybersecurity in Healthcare (Hardback) 9781394229796","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eArtificial Intelligence and Cybersecurity in Healthcare\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eRashmi Agrawal (Edited by), Agrawal (Author), Pramod Singh Rathore (Edited by), Ganesh Gopal Deverajan (Edited by), Rajiva Ranjan Divivedi (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394229796, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 21 March 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e25 x 15 x 1.5 cm, 0.907 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eArtificial Intelligence and Cybersecurity in Healthcare\u003c\/b\u003e\u003c\/i\u003e 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.\u003ci\u003e\u003c\/i\u003e \u003c\/p\u003e\n\u003cp\u003eAs 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. \u003c\/p\u003e\n\u003cp\u003eAI 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. \u003c\/p\u003e\n\u003cp\u003eSignificant 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. \u003c\/p\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePreeti Narooka and Deepa Parasar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Literature Review 3\u003c\/p\u003e \u003cp\u003e1.2.1 Research Paper Survey 3\u003c\/p\u003e \u003cp\u003e1.2.2 Existing System Methodologies 5\u003c\/p\u003e \u003cp\u003e1.2.3 Comparative Analysis 6\u003c\/p\u003e \u003cp\u003e1.2.3.1 Google Cloud Speech-to-Text API 7\u003c\/p\u003e \u003cp\u003e1.2.3.2 Microsoft Azure Speech Services 7\u003c\/p\u003e \u003cp\u003e1.2.3.3 IBM Watson Speech to Text 7\u003c\/p\u003e \u003cp\u003e1.2.3.4 CMU Sphinx 7\u003c\/p\u003e \u003cp\u003e1.3 Proposed System 8\u003c\/p\u003e \u003cp\u003e1.4 Implementation and Results 11\u003c\/p\u003e \u003cp\u003e1.5 Conclusion 14\u003c\/p\u003e \u003cp\u003eReferences 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Securing IoMT-Based Healthcare System: Issues, Challenges, and Solutions 17\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAshok Kumar, Rahul Gupta, Sunil Kumar, Kamlesh Dutta and Mukesh Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 18\u003c\/p\u003e \u003cp\u003e2.1.1 Motivation for the Study 19\u003c\/p\u003e \u003cp\u003e2.2 Related Work 20\u003c\/p\u003e \u003cp\u003e2.3 SHS Architecture, Applications, and Challenges 23\u003c\/p\u003e \u003cp\u003e2.3.1 Applications of the Smart Healthcare System 24\u003c\/p\u003e \u003cp\u003e2.3.2 Open Key Challenges 26\u003c\/p\u003e \u003cp\u003e2.4 Security Issues in SHS 30\u003c\/p\u003e \u003cp\u003e2.5 Security Solutions\/Techniques Proposed by Researchers 33\u003c\/p\u003e \u003cp\u003e2.6 Future Research Directions 48\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 50\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fog Computing in Healthcare: Enhancing Security and Privacy in Distributed Systems 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDeepa Arora and Oshin Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 58\u003c\/p\u003e \u003cp\u003e3.1.1 Applications of Fog Computing in Healthcare 61\u003c\/p\u003e \u003cp\u003e3.1.2 Technical Details of Implementing Fog Computing in Healthcare System 63\u003c\/p\u003e \u003cp\u003e3.2 Case Studies 65\u003c\/p\u003e \u003cp\u003e3.2.1 Case Study 1: Remote Monitoring of Patients Using Fog Computing 66\u003c\/p\u003e \u003cp\u003e3.2.2 Case Study 2: Fog Computing in Clinical Decision Support 67\u003c\/p\u003e \u003cp\u003e3.2.3 Case Study 3: Smart Health 2.0 Project in China 70\u003c\/p\u003e \u003cp\u003e3.3 Challenges 73\u003c\/p\u003e \u003cp\u003e3.4 Methods to Enhance Security and Privacy in Distributed Systems 74\u003c\/p\u003e \u003cp\u003e3.5 Future Directions of Fog Computing in Healthcare 80\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 81\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Blockchain Technology for Securing Healthcare Data in Cyber-Physical Systems 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHimanshu Rastogi, Abhay Narayan Tripathi and Bharti Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 What is Healthcare Data? 86\u003c\/p\u003e \u003cp\u003e4.1.1 Technologies in Healthcare 88\u003c\/p\u003e \u003cp\u003e4.1.1.1 IoT for Healthcare 88\u003c\/p\u003e \u003cp\u003e4.1.1.2 Online Healthcare 88\u003c\/p\u003e \u003cp\u003e4.1.1.3 Big Data in Healthcare 89\u003c\/p\u003e \u003cp\u003e4.1.1.4 Artificial Intelligence in Healthcare 90\u003c\/p\u003e \u003cp\u003e4.2 Need of Maintaining Healthcare Data 91\u003c\/p\u003e \u003cp\u003e4.3 Risk Associated with Healthcare Data 92\u003c\/p\u003e \u003cp\u003e4.4 Cyber-Physical Systems (CPS) 93\u003c\/p\u003e \u003cp\u003e4.5 Healthcare Cyber-Physical Systems (HCPS) 97\u003c\/p\u003e \u003cp\u003e4.6 Blockchain Technology 99\u003c\/p\u003e \u003cp\u003e4.6.1 Block Structure 101\u003c\/p\u003e \u003cp\u003e4.6.2 Hashing and Digital Signature 102\u003c\/p\u003e \u003cp\u003e4.7 Blockchain Technology in Healthcare Data 103\u003c\/p\u003e \u003cp\u003e4.8 Blockchain-Enabled Cyber-Physical Systems (CPS) 106\u003c\/p\u003e \u003cp\u003e4.9 Conclusion 108\u003c\/p\u003e \u003cp\u003eReferences 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Augmented Reality and Virtual Reality in Healthcare: Advancements and Security Challenges 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSrinivas Kumar Palvadi, Pradeep K. G. M., D. Rammurthy, G. Kadiravan and M. M. Prasada Reddy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 114\u003c\/p\u003e \u003cp\u003eAdvancements 115\u003c\/p\u003e \u003cp\u003eSecurity Challenges 118\u003c\/p\u003e \u003cp\u003eWhat is Augmented Reality? 123\u003c\/p\u003e \u003cp\u003eWhat is Virtual Reality? 129\u003c\/p\u003e \u003cp\u003eRevent Developments in AR and VR 137\u003c\/p\u003e \u003cp\u003eAugmented Reality in Ecommerce 138\u003c\/p\u003e \u003cp\u003eVirtual Reality in Healthcare 138\u003c\/p\u003e \u003cp\u003eAugmented Reality in Advertising 138\u003c\/p\u003e \u003cp\u003eVirtual Reality in Education 138\u003c\/p\u003e \u003cp\u003eResearch Problems in AR and VR in Healthcare 138\u003c\/p\u003e \u003cp\u003eUser Experience 139\u003c\/p\u003e \u003cp\u003eEffectiveness 139\u003c\/p\u003e \u003cp\u003eIntegration with Clinical Workflow 139\u003c\/p\u003e \u003cp\u003eData Security and Privacy 140\u003c\/p\u003e \u003cp\u003eCost-Effectiveness 140\u003c\/p\u003e \u003cp\u003eChallenges in AR and VR in Healthcare 140\u003c\/p\u003e \u003cp\u003eData Privacy and Security 140\u003c\/p\u003e \u003cp\u003eCost 140\u003c\/p\u003e \u003cp\u003eTechnical Issues 141\u003c\/p\u003e \u003cp\u003eIntegration with Existing Systems 141\u003c\/p\u003e \u003cp\u003eTraining and Education 141\u003c\/p\u003e \u003cp\u003eLegal and Ethical Considerations 141\u003c\/p\u003e \u003cp\u003eFuture Research in AR and VR 141\u003c\/p\u003e \u003cp\u003eUser Experience 142\u003c\/p\u003e \u003cp\u003eHealth Applications 142\u003c\/p\u003e \u003cp\u003eEducation and Training 142\u003c\/p\u003e \u003cp\u003eTechnical Advancements 142\u003c\/p\u003e \u003cp\u003eEthical and Legal Implications 142\u003c\/p\u003e \u003cp\u003eSecurity Challenges in AR and VR 143\u003c\/p\u003e \u003cp\u003eData Privacy 143\u003c\/p\u003e \u003cp\u003eMalware and Viruses 143\u003c\/p\u003e \u003cp\u003eUser Safety 143\u003c\/p\u003e \u003cp\u003eIntellectual Property Theft 143\u003c\/p\u003e \u003cp\u003eCybersecurity Vulnerabilities 143\u003c\/p\u003e \u003cp\u003eSocial Engineering 143\u003c\/p\u003e \u003cp\u003eDevice and Network Security 144\u003c\/p\u003e \u003cp\u003eConclusion 144\u003c\/p\u003e \u003cp\u003eReferences 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Next Generation Healthcare: Leveraging AI for Personalized Diagnosis, Treatment, and Monitoring 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSuraj Shukla and Brijesh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 147\u003c\/p\u003e \u003cp\u003e6.2 Benefits of AI in Healthcare 149\u003c\/p\u003e \u003cp\u003e6.2.1 Personalized Diagnosis and Treatment 149\u003c\/p\u003e \u003cp\u003e6.2.2 Improved Diagnostic Accuracy and Speed 150\u003c\/p\u003e \u003cp\u003e6.2.3 Accelerated Drug Discovery 151\u003c\/p\u003e \u003cp\u003e6.2.4 Remote Monitoring and Early Detection 152\u003c\/p\u003e \u003cp\u003e6.3 Challenges of AI in Healthcare 153\u003c\/p\u003e \u003cp\u003e6.3.1 Data Privacy and Security 153\u003c\/p\u003e \u003cp\u003e6.3.1.1 Data Encryption 154\u003c\/p\u003e \u003cp\u003e6.3.1.2 Access Controls 154\u003c\/p\u003e \u003cp\u003e6.3.1.3 Data Anonymization 155\u003c\/p\u003e \u003cp\u003e6.3.1.4 Secure Infrastructure 155\u003c\/p\u003e \u003cp\u003e6.3.1.5 Compliance with Regulations 155\u003c\/p\u003e \u003cp\u003e6.3.2 Algorithmic Transparency and Interpretability 155\u003c\/p\u003e \u003cp\u003e6.3.2.1 Explainable AI (XAI) Techniques 156\u003c\/p\u003e \u003cp\u003e6.3.2.2 Standardized Reporting 156\u003c\/p\u003e \u003cp\u003e6.3.2.3 Ethical Considerations 156\u003c\/p\u003e \u003cp\u003e6.3.2.4 Regulatory Framework 156\u003c\/p\u003e \u003cp\u003e6.3.3 Ethical Considerations 157\u003c\/p\u003e \u003cp\u003e6.3.4 Limited Generalizability 159\u003c\/p\u003e \u003cp\u003e6.3.5 Regulatory and Legal Frameworks 160\u003c\/p\u003e \u003cp\u003e6.3.6 Cyber Threat 161\u003c\/p\u003e \u003cp\u003e6.4 Approaches to Addressing Challenges in AI in Healthcare 162\u003c\/p\u003e \u003cp\u003e6.4.1 Data Privacy and Security Measures 162\u003c\/p\u003e \u003cp\u003e6.4.2 Algorithmic Transparency and Interpretability Techniques 162\u003c\/p\u003e \u003cp\u003e6.4.3 Ethical Frameworks and Guidelines 163\u003c\/p\u003e \u003cp\u003e6.4.4 Strategies for Enhancing Generalizability 163\u003c\/p\u003e \u003cp\u003e6.4.5 Regulatory and Legal Frameworks 163\u003c\/p\u003e \u003cp\u003e6.5 Case Studies and Applications of AI in Healthcare 163\u003c\/p\u003e \u003cp\u003e6.5.1 Diagnosing Diseases with AI 163\u003c\/p\u003e \u003cp\u003e6.5.2 Predictive Analytics for Patient Monitoring 164\u003c\/p\u003e \u003cp\u003e6.5.3 Personalized Treatment Recommendations 164\u003c\/p\u003e \u003cp\u003e6.5.4 AI-Assisted Robotic Surgery 164\u003c\/p\u003e \u003cp\u003e6.5.5 Drug Discovery and Development 164\u003c\/p\u003e \u003cp\u003e6.5.5.1 Target Identification and Validation 165\u003c\/p\u003e \u003cp\u003e6.5.5.2 Virtual Screening and Drug Design 165\u003c\/p\u003e \u003cp\u003e6.5.5.3 Drug Repurposing 165\u003c\/p\u003e \u003cp\u003e6.5.5.4 Predictive Toxicology and Safety Assessment 165\u003c\/p\u003e \u003cp\u003e6.5.5.5 Clinical Trial Optimization 166\u003c\/p\u003e \u003cp\u003e6.5.5.6 Real-Time Monitoring and Surveillance 166\u003c\/p\u003e \u003cp\u003e6.5.5.7 Data Integration and Analysis 166\u003c\/p\u003e \u003cp\u003e6.5.6 Virtual Assistants and Chatbots 166\u003c\/p\u003e \u003cp\u003e6.6 Future Directions and Opportunities in AI for Healthcare 166\u003c\/p\u003e \u003cp\u003e6.6.1 Integration of AI with Precision Medicine 167\u003c\/p\u003e \u003cp\u003e6.6.2 AI-Powered Drug Discovery and Development 167\u003c\/p\u003e \u003cp\u003e6.6.3 Augmented Decision Support Systems 167\u003c\/p\u003e \u003cp\u003e6.6.4 Telehealth and Remote Patient Monitoring 168\u003c\/p\u003e \u003cp\u003e6.6.5 Explainable AI and Ethical Considerations 168\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 168\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Exploring the Advantages and Security Aspects of Digital Twin Technology in Healthcare 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSrinivas Kumar Palvadi, Pradeep K. G. M. and G. Kadiravan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 174\u003c\/p\u003e \u003cp\u003e7.2 Benefits 176\u003c\/p\u003e \u003cp\u003e7.3 Security Considerations 179\u003c\/p\u003e \u003cp\u003e7.4 Contribution in this Domain to Healthcare 184\u003c\/p\u003e \u003cp\u003e7.5 Medical Device Development 186\u003c\/p\u003e \u003cp\u003e7.6 Digital Twin Technology in Healthcare in Future 187\u003c\/p\u003e \u003cp\u003e7.7 Continuous UI Upgrades 193\u003c\/p\u003e \u003cp\u003e7.7.1 Getting Started with this Domain in Healthcare 193\u003c\/p\u003e \u003cp\u003e7.7.2 Future Challenges in the Field 193\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 194\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An Extensive Study of AI and Cybersecurity in Healthcare 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHemlata, Manish Rai and Utsav Krishan Murari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 208\u003c\/p\u003e \u003cp\u003e8.1.1 Speculating About the Use of AI in Medical Care in the Future 209\u003c\/p\u003e \u003cp\u003e8.1.2 Managing the Exchange of Information 211\u003c\/p\u003e \u003cp\u003e8.1.3 Considering that Governments Function as Strategic Actors 211\u003c\/p\u003e \u003cp\u003e8.1.4 Cybersecurity 213\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 213\u003c\/p\u003e \u003cp\u003e8.3 Methodology 215\u003c\/p\u003e \u003cp\u003e8.4 AI Cybersecurity’s Significance for Healthcare 216\u003c\/p\u003e \u003cp\u003e8.5 Difficulties with AI Cybersecurity 217\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 218\u003c\/p\u003e \u003cp\u003eReferences 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Cloud Computing in Healthcare: Risks and Security Measures 221\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNeha Gupta, Rashmi Agrawal and Kavita Arora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 222\u003c\/p\u003e \u003cp\u003eCurrent State of Healthcare Industry 223\u003c\/p\u003e \u003cp\u003eCloud Computing in Healthcare 225\u003c\/p\u003e \u003cp\u003eBenefits of Adopting Cloud in Healthcare 226\u003c\/p\u003e \u003cp\u003eDrivers for Cloud Adoption in Healthcare 230\u003c\/p\u003e \u003cp\u003eCloud Challenges in Healthcare 232\u003c\/p\u003e \u003cp\u003eCloud Computing–Based Healthcare Services 235\u003c\/p\u003e \u003cp\u003eCurrent Market Dynamics 237\u003c\/p\u003e \u003cp\u003eImpact of Cloud Computing in Indian Healthcare Firms 239\u003c\/p\u003e \u003cp\u003eConclusion 240\u003c\/p\u003e \u003cp\u003eReferences 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Explainable Artificial Intelligence in Healthcare: Transparency and Trustworthiness 243\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSakshi and Gunjan Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 244\u003c\/p\u003e \u003cp\u003e10.1.1 Role of XAI in AI 245\u003c\/p\u003e \u003cp\u003e10.1.1.1 Explain to Justify 245\u003c\/p\u003e \u003cp\u003e10.1.1.2 Explain to Control 246\u003c\/p\u003e \u003cp\u003e10.1.1.3 Explain to Discover 246\u003c\/p\u003e \u003cp\u003e10.1.1.4 Explain to Improve 246\u003c\/p\u003e \u003cp\u003e10.1.2 Importance of Explainable Artificial Intelligence 247\u003c\/p\u003e \u003cp\u003e10.1.2.1 Understanding the Need for Explainability 247\u003c\/p\u003e \u003cp\u003e10.1.2.2 Benefits of XAI in Healthcare 248\u003c\/p\u003e \u003cp\u003e10.1.3 Addressing the Challenges of XAI Adoption 250\u003c\/p\u003e \u003cp\u003e10.1.3.1 Complexity of AI Models 251\u003c\/p\u003e \u003cp\u003e10.1.3.2 Trade-Offs Between Accuracy and Interpretability 251\u003c\/p\u003e \u003cp\u003e10.1.3.3 Ensuring Generalizability and Robustness 251\u003c\/p\u003e \u003cp\u003e10.2 Working of XAI in Healthcare 251\u003c\/p\u003e \u003cp\u003e10.2.1 Data Collection 252\u003c\/p\u003e \u003cp\u003e10.3 Explorable Artificial Intelligence Techniques and Methods in Healthcare 253\u003c\/p\u003e \u003cp\u003e10.3.1 Rule-Based Systems 254\u003c\/p\u003e \u003cp\u003e10.3.2 Interpretable Machine Learning Models 254\u003c\/p\u003e \u003cp\u003e10.3.3 Visualizations (e.g., Heatmaps) 255\u003c\/p\u003e \u003cp\u003e10.3.4 Model-Agnostic Methods (e.g., LIME, SHAP) 255\u003c\/p\u003e \u003cp\u003e10.4 Interpretable Deep Learning Models 256\u003c\/p\u003e \u003cp\u003e10.4.1 Attention Mechanisms 256\u003c\/p\u003e \u003cp\u003e10.4.2 Saliency Maps 257\u003c\/p\u003e \u003cp\u003e10.4.3 Concept Activation Vectors 257\u003c\/p\u003e \u003cp\u003e10.4.4 Layer-Wise Relevance Propagation 257\u003c\/p\u003e \u003cp\u003e10.4.5 Rule Extraction 257\u003c\/p\u003e \u003cp\u003e10.4.6 Model Visualization Techniques 258\u003c\/p\u003e \u003cp\u003e10.5 Clinical Decision Support System 258\u003c\/p\u003e \u003cp\u003e10.6 Explainable Clinical Natural Language Processing 259\u003c\/p\u003e \u003cp\u003e10.6.1 Interpretability Techniques for Clinical Text Classification 260\u003c\/p\u003e \u003cp\u003e10.6.2 Explaining Named Entity Recognition in Clinical NLP 261\u003c\/p\u003e \u003cp\u003e10.6.3 Enhancing Interpretability in Medical Coding 261\u003c\/p\u003e \u003cp\u003e10.7 User-Centered Design of XAI Systems 262\u003c\/p\u003e \u003cp\u003e10.8 Regulatory and Legal Perspectives in XAI for Healthcare 264\u003c\/p\u003e \u003cp\u003e10.8.1 Regulations 265\u003c\/p\u003e \u003cp\u003e10.8.2 Legal Framework 265\u003c\/p\u003e \u003cp\u003e10.8.3 Data Governance and Privacy Regulations 265\u003c\/p\u003e \u003cp\u003e10.8.4 Model Transparency and Accountability 266\u003c\/p\u003e \u003cp\u003e10.8.5 Algorithmic Bias and Fairness 266\u003c\/p\u003e \u003cp\u003e10.8.6 Explainability and Interpretability 266\u003c\/p\u003e \u003cp\u003e10.8.7 Ethical and Legal Responsibility 266\u003c\/p\u003e \u003cp\u003e10.9 Ethical Considerations in Explainable Artificial Intelligence (XAI) for Healthcare 267\u003c\/p\u003e \u003cp\u003e10.9.1 Bias and Fairness 267\u003c\/p\u003e \u003cp\u003e10.9.2 Privacy and Informed Consent 268\u003c\/p\u003e \u003cp\u003e10.9.3 Security and Protection Against Adversarial Attacks 268\u003c\/p\u003e \u003cp\u003e10.10 Strategies for Promotion of Accountable Use of XAI in Healthcare 268\u003c\/p\u003e \u003cp\u003e10.10.1 Explainability and Transparency 269\u003c\/p\u003e \u003cp\u003e10.10.2 Human-AI Collaboration and Shared Decision-Making 269\u003c\/p\u003e \u003cp\u003e10.10.3 Regulatory Frameworks and Ethical Guidelines 269\u003c\/p\u003e \u003cp\u003e10.10.4 Continuous Monitoring and Evaluation 270\u003c\/p\u003e \u003cp\u003eConclusion 270\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Fuzzy Expert System to Diagnose the Heart Disease Risk Level 273\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Lakshmi, K. Sarath, K. Parish Venkata Kumar, G. Praveen, B. Karthik and Y. Phani Bhushan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 274\u003c\/p\u003e \u003cp\u003e11.2 Work Related 275\u003c\/p\u003e \u003cp\u003e11.3 Expert Methods for Medical Diagnosis 276\u003c\/p\u003e \u003cp\u003e11.4 Parameter Input 277\u003c\/p\u003e \u003cp\u003e11.4.1 Cholesterol 277\u003c\/p\u003e \u003cp\u003e11.4.2 Blood Pressure (BP) 278\u003c\/p\u003e \u003cp\u003e11.4.3 Sugar Blood 278\u003c\/p\u003e \u003cp\u003e11.4.4 Rate of Heart 279\u003c\/p\u003e \u003cp\u003e11.4.5 Glucose Meter 279\u003c\/p\u003e \u003cp\u003e11.4.6 Monitor Blood Pressure 279\u003c\/p\u003e \u003cp\u003e11.5 System Flow 279\u003c\/p\u003e \u003cp\u003e11.5.1 Input and Output of Fuzzy 280\u003c\/p\u003e \u003cp\u003e11.5.2 System Workflow Based on Fuzzy 280\u003c\/p\u003e \u003cp\u003e11.5.3 Data Set 280\u003c\/p\u003e \u003cp\u003e11.6 Simulation and Result 281\u003c\/p\u003e \u003cp\u003e11.6.1 Accuracy Level of Expert System 284\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 285\u003c\/p\u003e \u003cp\u003eReferences 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Search and Rescue–Based Sparse Auto‐Encoder for Detecting Heart Disease in IoT Healthcare Environment 289\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRakesh Chandrashekar, B. Gunapriya and Balasubramanian Prabhu Kavin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 290\u003c\/p\u003e \u003cp\u003e12.2 Related Works 291\u003c\/p\u003e \u003cp\u003e12.3 Proposed Model 294\u003c\/p\u003e \u003cp\u003e12.3.1 Dataset Description 294\u003c\/p\u003e \u003cp\u003e12.3.2 Pre-Processing 294\u003c\/p\u003e \u003cp\u003e12.3.3 Feature Selection Using Artificial Fish Swarm Optimization (AFO) 296\u003c\/p\u003e \u003cp\u003e12.3.3.1 Prey Behavior 296\u003c\/p\u003e \u003cp\u003e12.3.3.2 Swarm Behavior 296\u003c\/p\u003e \u003cp\u003e12.3.3.3 Follow Behavior 297\u003c\/p\u003e \u003cp\u003e12.3.4 Prediction of Heart Disease Using ISAE Model 297\u003c\/p\u003e \u003cp\u003e12.3.4.1 Design of the SRO Algorithm 298\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 301\u003c\/p\u003e \u003cp\u003e12.4.1 An Experimental Setup Details 301\u003c\/p\u003e \u003cp\u003e12.4.2 Experiment System Characteristics 302\u003c\/p\u003e \u003cp\u003e12.4.3 Performance Metrics 302\u003c\/p\u003e \u003cp\u003e12.5 Conclusion and Future Work 306\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Growth Optimization–Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment 311\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJayasheel Kumar Kalagatoori Archakam, Santosh Kumar B. and Balasubramanian Prabhu Kavin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 312\u003c\/p\u003e \u003cp\u003e13.2 Related Works 313\u003c\/p\u003e \u003cp\u003e13.2.1 Challenges 315\u003c\/p\u003e \u003cp\u003e13.3 Proposed Model 315\u003c\/p\u003e \u003cp\u003e13.3.1 Overall IoMT-Based Basis 315\u003c\/p\u003e \u003cp\u003e13.3.2 Proposed Methodology 316\u003c\/p\u003e \u003cp\u003e13.3.2.1 Stacked Bidirectional LSTM RNN for Disease Prediction 317\u003c\/p\u003e \u003cp\u003e13.3.2.2 Growth Optimizer 318\u003c\/p\u003e \u003cp\u003e13.4 Results and Discussion 320\u003c\/p\u003e \u003cp\u003e13.4.1 Dataset 321\u003c\/p\u003e \u003cp\u003e13.4.1.1 Wisconsin Breast Cancer Dataset 321\u003c\/p\u003e \u003cp\u003e13.4.2 Model Assessment 321\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 325\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Lightweight Fuzzy Logical MQTT Security System to Secure the Low Configurated Medical Device System by Communicating the IoT 329\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBasi Reddy A., Kanegonda Ravi Chythanya, Sharada K. A. and R. Senthamil Selvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 330\u003c\/p\u003e \u003cp\u003e14.2 Methodology of FLS 331\u003c\/p\u003e \u003cp\u003e14.3 Problem Identification 332\u003c\/p\u003e \u003cp\u003e14.3.1 Framework 332\u003c\/p\u003e \u003cp\u003e14.3.1.1 Threat Modelling 333\u003c\/p\u003e \u003cp\u003e14.3.1.2 Attack Outline 333\u003c\/p\u003e \u003cp\u003e14.3.1.3 Design Idea 333\u003c\/p\u003e \u003cp\u003e14.4 Proposed Approach 334\u003c\/p\u003e \u003cp\u003e14.5 Result with Discussion 335\u003c\/p\u003e \u003cp\u003e14.5.1 Intrusion Detection System Analysis Metrics 336\u003c\/p\u003e \u003cp\u003e14.5.1.1 Threat Detection Efficiency 336\u003c\/p\u003e \u003cp\u003e14.5.1.2 Threat Detection Rate 336\u003c\/p\u003e \u003cp\u003e14.5.1.3 Threat Detection Accuracy (TDA) Ratio 340\u003c\/p\u003e \u003cp\u003e14.5.1.4 False vs. Positive Rate (FPR) 340\u003c\/p\u003e \u003cp\u003e14.5.2 Communication Rate 340\u003c\/p\u003e \u003cp\u003e14.5.2.1 Precision 342\u003c\/p\u003e \u003cp\u003e14.5.2.2 Recall 342\u003c\/p\u003e \u003cp\u003e14.5.2.3 F-Score 342\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 344\u003c\/p\u003e \u003cp\u003eReferences 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 IoT-Based Secured Biomedical Device to Remote Monitoring to the Patient 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDinesh G., Jeevanarao Batakala, Yousef A. Baker El-Ebiary and N. Ashokkumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 350\u003c\/p\u003e \u003cp\u003e15.2 Internet of Things 353\u003c\/p\u003e \u003cp\u003e15.3 IoMT 354\u003c\/p\u003e \u003cp\u003e15.3.1 Real Application of IoT 354\u003c\/p\u003e \u003cp\u003e15.3.2 Ransomware 355\u003c\/p\u003e \u003cp\u003e15.3.2.1 Target and Ransomware Implications 356\u003c\/p\u003e \u003cp\u003e15.3.2.2 How Ransomware Works 356\u003c\/p\u003e \u003cp\u003e15.4 Biostatistical Techniques for Maintaining Security Goals 356\u003c\/p\u003e \u003cp\u003e15.5 Healthcare IT System Through Biometric BioMT Approach 357\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Fuzzy Interface Drug Delivery Decision-Making Algorithm 365\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYogendra Narayan, Mukta Sandhu, Yousef A. Baker El-Ebiary and N. Ashokkumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 366\u003c\/p\u003e \u003cp\u003e16.2 Description and Problems 367\u003c\/p\u003e \u003cp\u003e16.3 Methods 367\u003c\/p\u003e \u003cp\u003e16.3.1 Tree Decision 369\u003c\/p\u003e \u003cp\u003e16.3.2 Fuzzy Inference System 370\u003c\/p\u003e \u003cp\u003e16.3.3 Fuzzification of Decision Rules of Tree 370\u003c\/p\u003e \u003cp\u003e16.3.4 FIS Decision Making 371\u003c\/p\u003e \u003cp\u003e16.4 Application of Analgesia 373\u003c\/p\u003e \u003cp\u003e16.4.1 Analgesia Nociception Index 373\u003c\/p\u003e \u003cp\u003e16.4.2 Data Collection\/Preprocessing 373\u003c\/p\u003e \u003cp\u003e16.5 Result 374\u003c\/p\u003e \u003cp\u003e16.5.1 FIS of Structure 374\u003c\/p\u003e \u003cp\u003e16.6 Discussion 376\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 377\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Implementation of Clinical Fuzzy‐Based Decision Supportive System to Monitor Renal Function 381\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Dinesh Kumar, M. J. D. Ebinezer and N. Ashokkumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 382\u003c\/p\u003e \u003cp\u003e17.1.1 Expert Systems of FIS 383\u003c\/p\u003e \u003cp\u003e17.1.2 Neuro Adaptive of FIS 384\u003c\/p\u003e \u003cp\u003e17.1.2.1 Fuzzification Layer, First Layer 385\u003c\/p\u003e \u003cp\u003e17.1.2.2 Law Layer, Second Layer 385\u003c\/p\u003e \u003cp\u003e17.1.2.3 Normalization Layer, Fourth Layer 385\u003c\/p\u003e \u003cp\u003e17.1.2.4 Defuzzification 385\u003c\/p\u003e \u003cp\u003e17.1.2.5 The Summation Layer, or Fifth Layer 385\u003c\/p\u003e \u003cp\u003e17.2 Work Related 386\u003c\/p\u003e \u003cp\u003e17.3 Methods 387\u003c\/p\u003e \u003cp\u003e17.3.1 MATLAB 391\u003c\/p\u003e \u003cp\u003e17.4 Discussion and Results 392\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 393\u003c\/p\u003e \u003cp\u003eReferences 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Deep Learning–Based Medical Image Classification and Web Application Framework to Identify Alzheimer’s Disease 397\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Parish Venkata Kumar, Piyush Charan, S. Kayalvili and M. V. B. T. Santhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 398\u003c\/p\u003e \u003cp\u003e18.2 Proposed Methodology 401\u003c\/p\u003e \u003cp\u003e18.2.1 Various Techniques Used 402\u003c\/p\u003e \u003cp\u003e18.3 Experiment Setup 404\u003c\/p\u003e \u003cp\u003e18.4 Result 405\u003c\/p\u003e \u003cp\u003e18.5 Discussion of Result 408\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 409\u003c\/p\u003e \u003cp\u003eReferences 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Using Deep Learning to Classify and Diagnose Alzheimer’s Disease 413\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eA. V. Sriharsha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 413\u003c\/p\u003e \u003cp\u003e19.2 Biomarkers and Detection of Alzheimer’s Disease 414\u003c\/p\u003e \u003cp\u003e19.2.1 AD Biomarkers 414\u003c\/p\u003e \u003cp\u003e19.2.2 Data Preprocessing 415\u003c\/p\u003e \u003cp\u003e19.2.3 Management of Data 416\u003c\/p\u003e \u003cp\u003e19.2.4 Patch Based 416\u003c\/p\u003e \u003cp\u003e19.3 Methods 417\u003c\/p\u003e \u003cp\u003e19.3.1 The E 2 AD 2 C Framework 417\u003c\/p\u003e \u003cp\u003e19.3.2 Data Normalization 420\u003c\/p\u003e \u003cp\u003e19.3.3 Methods and Technique 420\u003c\/p\u003e \u003cp\u003e19.4 Model Evaluation and Methods 422\u003c\/p\u003e \u003cp\u003e19.4.1 Checking the Web Services 423\u003c\/p\u003e \u003cp\u003e19.4.2 Other Fuzzy Systems of Diagnosis of Diseases 424\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 425\u003c\/p\u003e \u003cp\u003eReferences 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Developing a Soft Computing Fuzzy Interface System for Peptic Ulcer Diagnosis 429\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. Lakshmi, K. Parish Venkata Kumar and N. Ashokkumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 430\u003c\/p\u003e \u003cp\u003e20.2 Methodology 431\u003c\/p\u003e \u003cp\u003e20.2.1 Animals 431\u003c\/p\u003e \u003cp\u003e20.2.2 Method Chemical of Gastric Ulcer 432\u003c\/p\u003e \u003cp\u003e20.2.3 Index Measurement of Ulcer 432\u003c\/p\u003e \u003cp\u003e20.2.4 Data Sets 432\u003c\/p\u003e \u003cp\u003e20.2.5 Fuzzy Expert System 433\u003c\/p\u003e \u003cp\u003e20.3 Results 434\u003c\/p\u003e \u003cp\u003e20.3.1 Variables of Input and Output 434\u003c\/p\u003e \u003cp\u003e20.3.2 Methods 435\u003c\/p\u003e \u003cp\u003e20.3.3 EOC Analysis 437\u003c\/p\u003e \u003cp\u003e20.3.4 Other Fuzzy Expert Systems for Disease Diagnosis 438\u003c\/p\u003e \u003cp\u003e20.4 Conclusion 439\u003c\/p\u003e \u003cp\u003eReferences 440\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Digital Twin Technology in Healthcare: Benefits and Security Considerations 443\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyanka Tyagi and Kajol Mittal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 444\u003c\/p\u003e \u003cp\u003eConclusion 457\u003c\/p\u003e \u003cp\u003eReferences 458\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Combating Cyber Threats Including Wormhole Attacks in Healthcare Cyber-Physical Systems: Advanced Prevention and Mitigation Techniques 461\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePramod Singh Rathore and Mrinal Kanti Sarkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction to Cybersecurity in Healthcare Cyber-Physical Systems 462\u003c\/p\u003e \u003cp\u003e22.2 Understanding Cyber Threats in Healthcare 463\u003c\/p\u003e \u003cp\u003e22.2.1 Types of Cyber Threats in Healthcare Systems 463\u003c\/p\u003e \u003cp\u003e22.2.2 Special Focus on Wormhole Attacks 464\u003c\/p\u003e \u003cp\u003e22.2.3 Case Studies: Recent Cyberattacks in Healthcare 464\u003c\/p\u003e \u003cp\u003e22.3 Vulnerabilities in Healthcare Cyber-Physical Systems 465\u003c\/p\u003e \u003cp\u003e22.3.1 Identifying Common Vulnerabilities 465\u003c\/p\u003e \u003cp\u003e22.3.2 Impact of Wormhole Attacks on Healthcare Systems 466\u003c\/p\u003e \u003cp\u003e22.3.3 Assessing Risks in Connected Medical Devices 466\u003c\/p\u003e \u003cp\u003e22.4 Advanced Prevention Techniques 466\u003c\/p\u003e \u003cp\u003e22.4.1 Implementing Robust Encryption Protocols 467\u003c\/p\u003e \u003cp\u003e22.4.2 Role of Firewalls and Intrusion Detection Systems 467\u003c\/p\u003e \u003cp\u003e22.4.3 Preventive Measures for Wormhole Attacks 467\u003c\/p\u003e \u003cp\u003e22.5 Mitigation Strategies for Cyber Threats 468\u003c\/p\u003e \u003cp\u003e22.5.1 Developing an Effective Incident Response Plan 468\u003c\/p\u003e \u003cp\u003e22.5.2 Strategies for Containing and Mitigating Wormhole Attacks 469\u003c\/p\u003e \u003cp\u003e22.5.3 Disaster Recovery and Business Continuity Planning 469\u003c\/p\u003e \u003cp\u003e22.6 Emerging Technologies and Future Trends 469\u003c\/p\u003e \u003cp\u003e22.6.1 The Role of Artificial Intelligence in Cybersecurity 470\u003c\/p\u003e \u003cp\u003e22.6.2 Blockchain for Secure Healthcare Data Management 470\u003c\/p\u003e \u003cp\u003e22.6.3 Future Challenges and Opportunities in Healthcare Cybersecurity 470\u003c\/p\u003e \u003cp\u003e22.7 Training and Awareness Programs 471\u003c\/p\u003e \u003cp\u003e22.7.1 Educating Healthcare Staff on Cybersecurity Best Practices 471\u003c\/p\u003e \u003cp\u003e22.7.2 Training Programs for Wormhole Attack Prevention 471\u003c\/p\u003e \u003cp\u003eReferences 472\u003c\/p\u003e \u003cp\u003eIndex 475\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433219715352,"sku":"9781394229796","price":168.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394229796.jpg?v=1784852092","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/artificial-intelligence-and-cybersecurity-in-healthcare-hardback-9781394229796","provider":"Freshly Printed Books","version":"1.0","type":"link"}