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Elsevier

AI-Driven Cybersecurity for Intelligent Healthcare Systems

  • 1st Edition - June 9, 2026
  • Latest edition
  • Editors: Balamurugan Balusamy, Prithi Samuel, Sunita Chand, Mahmoud Ahmad Al-Khasawneh
  • Language: English

AI-Driven Cybersecurity for Intelligent Healthcare Systems explores the intersection between AI, cybersecurity, and healthcare. The book offers detailed insights into the unique… Read more

Description

AI-Driven Cybersecurity for Intelligent Healthcare Systems explores the intersection between AI, cybersecurity, and healthcare. The book offers detailed insights into the unique cybersecurity challenges faced by the healthcare sector and the role of AI in addressing these challenges. It presents case studies and real-world applications to illustrate the effectiveness of these solutions and highlights the significance of data privacy in healthcare and methods to ensure secure data sharing and storage. Topics such as federated learning, homomorphic encryption, and blockchain technology are covered to demonstrate how AI can enhance data security without compromising patient privacy.

This book will be an essential resource for anyone involved in the healthcare industry, offering practical solutions and fostering a more in-depth understanding of how AI can revolutionize cybersecurity in healthcare.

Key features

  • Includes case studies and real-world applications to illustrate the effectiveness of intelligent cybersecurity solutions
  • Discusses the increasing integration of artificial intelligence (AI) in healthcare systems, highlighting its role in enhancing medical diagnosis, treatment planning, and patient care
  • Focuses on advanced techniques for enhancing data security while maintaining patient privacy

Readership

Researchers and Academics in fields such as biomedical engineering, computer science, information technology, and healthcare informatics who study the intersection of AI, cybersecurity, and healthcare; graduates and undergraduates in disciplines such as healthcare management, health informatics, computer science, and cybersecurity looking to deepen their understanding of these areas

Table of contents

1. Smart Healthcare Systems with Improved Cybersecurity Through AI Integration
  • Introduction
  • Attack Surfaces and IoMT in Healthcare
  • Comparative Analysis: Healthcare vs. Other Critical Sectors
  • Cybersecurity Maturity Models in Healthcare
  • Real-World Case Studies
  • From Reactive to Proactive: Building Resilient Cybersecurity
  • Future of Healthcare Cybersecurity: AI, Blockchain, Quantum, and Beyond
  • Conclusion and Future Recommendations
2. Transforming Healthcare: Harnessing the Power of AI in the Modern Era
  • Introduction
  • AI Technologies in Healthcare
  • Applications of AI in Healthcare
  • Ethical and Legal Considerations in AI for Healthcare
  • Challenges in Implementation
  • Conclusion
3. Machine Learning-Enhanced Threat Intelligence for Advanced Cybersecurity
  • Introduction
  • ML Threat Detection and Its Impact on Cybersecurity
  • Leveraging ML in Cyber Threat Intelligence
  • Need for Advanced Threat Detection and Defense in Cyberspace
  • Traditional Threat Detection and Defense Mechanisms: Limitations and Drawbacks
  • ML for Threat Detection in Cybersecurity
  • Multilayered Threat Intelligence Framework Architecture
  • Proactive Threat Hunting Strategies
  • Challenges and Limitations
  • Case Study: Using ML for Threat Detection
  • Conclusion
4. Progress and Prospects of Blockchain for Healthcare
  • Motivation
  • Literature Review
  • Challenges and Limitations
  • Application Domains of Blockchain in Healthcare
  • Secure EMR Storage and Sharing Using Blockchain Technology
  • Secure Sharing of EMRs
  • Blockchain-AI Synergy in Smart Healthcare Systems
  • Readiness Assessment Framework for Blockchain Adoption in Healthcare
  • Discussion and Future Directions
  • Future Research Directions
  • Conclusion
5. Using AI to Improve the Security of the Internet of Medical Things: A Comprehensive Analysis
  • Introduction to IoMT and Its Security Landscape
  • Common Cyber Threats and Vulnerabilities in IoMT Systems
  • AI-Based Intrusion Detection and Prevention Systems
  • ML Models for Anomaly and Malware Detection
  • Deep Learning Architectures for Securing Medical Data Streams
  • Federated Learning for Privacy-Preserving IoMT Security
  • AI for Device Authentication and Access Control
  • Conclusion
6. Enhancing Cybersecurity Strategies for Multicloud Healthcare Systems Through Deep Learning-Based Threat Detection and Mitigation
  • Introduction
  • Literature Review
  • Methodology
  • Data Analysis
  • Findings
  • Discussion
  • Conclusion
7. Intelligent Access Control Mechanisms in Cybersecurity: A Technique for Health Data Security
  • Introduction
  • Literature Review and Related Work
  • Proposed Framework
  • Conclusion
  • Future Scope
  • Challenges
8. AI-Driven Threat Detection and Response: A Paradigm Shift in Cybersecurity
  • Introduction
  • Understanding Predictive Analytics in Cybersecurity
  • Conclusion
9. Blockchain for Healthcare: Securing Patient Data and Enabling Trusted Artificial Intelligence
  • Introduction
  • Blockchain Architecture
  • Importance of Blockchain in Healthcare
  • Key Capabilities of Blockchain Technology in Global Healthcare
  • Integrated Workflow Framework for Blockchain Implementation in Healthcare Services
  • Evolution and Applications of Blockchain Technology in Healthcare
  • Applications of Blockchain in Healthcare
  • Limitations and Future Scope
  • Conclusion
10. Deep Learning Techniques for Securing Data in Healthcare Applications
  • Introduction
  • DNN Applications in Healthcare IoT
  • Security Requirements in DNN Healthcare Systems
  • Key Data Security Challenges
  • Case Studies
  • Defense Strategies
  • Conclusion
11. Chatbot Systems in Healthcare: AI Integration and Security Measures
  • Introduction
  • Objectives of an AI-Assisted Telemedicine System
  • Purpose and Scope of Healthcare Chatbot Monitoring Systems
  • Applicability in the Healthcare Industry
  • Analysis of Existing Technologies
  • Challenges in Telemedicine Systems
  • Proposed AI-Assisted Telemedicine Framework
  • System Design
  • Implementation Approaches
  • Testing
  • Results, Discussion, and Performance Analysis
  • Applications and Conclusion
12. A Comprehensive Survey of Smart Medical Wearables in Fitness and Health Monitoring
  • Introduction to Smart Medical Wearables
  • Types of Smart Medical Wearables
  • Architectural Foundations of Smart Wearable Devices
  • Computational Intelligence in Smart Wearables
  • Data Management in Smart Wearables
  • Advanced Healthcare Applications of Smart Wearables
  • Emerging Technological Integrations in Smart Wearables
  • Technical Challenges and Limitations
  • Comparative Analysis of Wearable Technologies
  • Case Studies and Real-World Applications
  • Conclusion
13. Recent Advancements in Emerging Technologies for Secure Healthcare Management Systems
  • Introduction
  • Relevance of the Topic
  • Key Research Questions and Problems Addressed
  • Literature Review
  • Methodology
  • Emerging Technologies in Healthcare Management Systems
  • Discussion and Findings
  • Suggestions and Recommendations
  • Future Research Directions
  • Conclusion
14. AI-Driven Risk Assessment Models for Predicting Healthcare Cybersecurity Threats in Real Time
  • Introduction
  • Current Trends in Healthcare Cybersecurity Threats
  • AI-Based Risk Assessment Models
  • Methodology
  • Results and Discussion
  • Strategic Analysis of AI Techniques in Healthcare Cybersecurity
  • Conclusion and Future Work
15. Responsible AI in Healthcare: Navigating Ethics and Governance Challenges
  • Introduction
  • The Necessity of Ethical AI
  • Mathematical Models for Responsible AI in Healthcare
  • Importance of Responsible AI
  • Upholding Ethical Standards
  • Government Frameworks
  • Navigating Legal Challenges
  • Mitigation Strategies
  • Conclusion

Product details

  • Edition: 1
  • Latest edition
  • Published: September 11, 2026
  • Language: English

About the editors

BB

Balamurugan Balusamy

Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor in the Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, blockchain, and data sciences

Affiliations and expertise
Shiv Nadar University, Delhi-NCR, India

PS

Prithi Samuel

Dr Prithi Samuel is currently working as an assistant professor in the Department of Computational Intelligence at SRM Institute of Science and Technology, Kattankulathur Campus, Chennai. She has completed her Ph.D. in Information and Communication Engineering from Anna University, Chennai. She has got over 15 years of teaching experience in reputed engineering colleges in Tamil Nadu. She is a pioneer researcher in the areas of Automata Theory, Machine Learning, Deep Learning, Computational Intelligence Techniques, and the Internet of Things. She has published more than 25 papers in leading SCI and Scopus Journals and more than 25 papers in International Conferences and published 1 book and more than 10 book chapters in Wiley, Taylor and Francis, Springer, and Elsevier and published 4 patents and 2 patent grants. She is an active IEEE, ACM Member and holds an ISTE and IAENG lifetime membership
Affiliations and expertise
Assistant Professor, Department of Computational Intelligence at SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India

SC

Sunita Chand

Sunita Chand is an assistant professor in the Department of Department of Computer Science, Hansraj College, University of Delhi. She is a pioneer researcher in the areas of image processing, cyber security, deep learning, natural language processing, and nature inspired algorithms. She has published papers in leading international journals and international conferences and published book chapters in IEEE, Springer, Inderscience, AIP, ACM and Elsevier.
Affiliations and expertise
Assistant Professor, Department of Computer Science, Hansraj College, University of Delhi, India

MA

Mahmoud Ahmad Al-Khasawneh

Mahmoud Ahmad Al-Khasawneh is a faculty member in the School of Computing Skyline University College, Sharjah UAE.

His scholarly pursuits span a diverse array of fields within computer science. He has authored numerous papers in esteemed, peer-reviewed journals across leading publishers such as IEEE, Springer, Wiley, Hindawi, and MDPI. His research interests encompass Security, Image Encryption, Wireless Networks, Blockchain, Internet of Things, and Big Data. With a commitment to advancing knowledge and solving contemporary challenges in these domains, he actively engages in research, teaching, and mentorship, contributing to the academic and professional development of his students and peers. Driven by a passion for innovation and a dedication to excellence, he continues to make significant contributions to the field, shaping the future of technology and its applications.
Affiliations and expertise
Faculty, School of Computing Skyline University College, Sharjah UAE