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Artificial Intelligence (AI) in Healthcare Information Systems - Security and Privacy Challenges

A Volume in Information Systems Engineering & Management (ISEM) book series, Springer-Verlag

Assoc. Prof.  Edlira Martiri
Faculty of Economy,
University of Tirana, Albania
Assoc. Prof. Narasimha Rao Vajjhala
Dean, Faculty of Engineering and Architecture,
University of New York Tirana
Assoc. Prof. Fisnik Dalipi
Faculty of Technology, Linnaeus University, Växjö & Kalmar, Sweden
Assoc. Prof. Bian Yang
Norwegian University of Science and Technology, NTNU, Norway

About the Book

This book will examine the complex and rapidly evolving intersection of healthcare, artificial intelligence (AI), and data security. The book will address the central research question: can we harness the power of AI without jeopardizing patient data and privacy? In an era where AI is transforming healthcare practices, from diagnosis and treatment to patient management and drug discovery, the book will explore the paramount security and privacy concerns in AI-driven healthcare. This comprehensive volume will navigate the intricate landscape of AI applications in healthcare, highlighting the crucial need to safeguard sensitive patient data and maintain trust in these technological advancements.

The chapters in this book will explore the ethical and legal issues surrounding patient data collection, storage, and utilization for AI development. This book will also highlight the hidden biases embedded in AI algorithms and their potential to perpetuate healthcare disparities. Understanding these biases is essential for fair and just healthcare practices. In addition, this book will explore the black box problem of AI decision-making and its implications for patient trust and accountability in AI-driven healthcare solutions. The chapters in this book will also navigate the ever-evolving threat landscape for healthcare information systems, from targeted ransomware attacks to insider threats. Strategies to safeguard patient data and maintain system integrity will also be explored. Further, this book aims to discover tools like differential privacy and federated learning. These innovations strike a delicate balance, enabling AI advancements while preserving data privacy and security. Moreover, this book will recommend data governance frameworks, access controls, and encryption protocols to fortify healthcare information systems against vulnerabilities. Finally, the topics covered in this book will assist patients through clear explanations of AI's role in healthcare and avenues for redressal, ensuring that patients remain informed and empowered.

Note: There are NO submission or acceptance fees for manuscripts submitted to this book publication. This book will be submitted for possible indexation to Scopus, Web of Science, and major indices.

Topics (Suggestive but not limited to)

The proposed tentative content for this book is given below:

  1. AI Landscape in Healthcare Information Systems

    • The Convergence of AI and Healthcare

    • Historical Evolution of AI in Healthcare

    • AI-Powered Healthcare Use Cases

    • Significance of Security and Privacy

  2. AI Technologies in Healthcare

    • AI in Diagnostics and Disease Prediction

    • AI-Driven Treatment Planning

    • AI-Enabled Drug Discovery

    • AI in Patient Care and Management

  3. Data Security in Healthcare

    • Data Breaches and Leaks in Healthcare

    • Regulatory Frameworks (HIPAA, GDPR)

    • Threat Landscape in Healthcare

    • Data Encryption and Protection

  4. Privacy Concerns in Healthcare AI

    • Patient Data Privacy

    • Ethical Considerations

    • Consent and Transparency

    • De-identification Techniques

  5. AI Model Security

    • Adversarial Attacks on AI Models

    • Secure Model Deployment

    • Model Monitoring and Updates

    • Explainability and Accountability

  6. Secure Data Sharing

    • Interoperability Challenges

    • Federated Learning

    • Privacy-Enhancing Technologies

    • Secure Data Exchange Protocols

    • Cross-Institutional Collaborations

  7. Trust in Healthcare AI

    • Building Trust with Patients

    • Trustworthiness of AI Systems

    • Human-AI Collaboration

    • Legal and Ethical Aspects

  8. Case Studies and Real-World Examples

    • AI Security Failures

    • Successful Privacy Implementation

    • Lessons Learned

  9. Future Directions and Emerging Trends

    • Evolving Threats and Solutions

    • Regulatory Changes

    • The Role of AI in Global Healthcare

  10. AI Ethics and Bias in Healthcare

    • Ethical Considerations in AI-Powered Healthcare

    • Addressing Bias in Healthcare AI Algorithms

    • Fairness and Equity in AI Diagnostics and Treatment

  11. AI-Driven Remote Healthcare and Telemedicine

    • Telehealth Revolution: AI in Remote Healthcare

    • Security Challenges in Telemedicine

    • Ensuring Privacy in Remote Healthcare Services

  12. AI-Enhanced Healthcare Analytics

    • Leveraging AI for Healthcare Data Analytics

    • Data Privacy in Healthcare Analytics

    • Extracting Insights while Preserving Security

  13. Healthcare AI Adoption and Implementation Challenges

    • Overcoming Barriers to AI Adoption in Healthcare

    • Ensuring Security in AI Implementation

    • Best Practices for Successful AI Integration

  14. Patient-Centric AI in Healthcare

    • Personalizing Patient Care with AI

    • Patient Empowerment through AI-Driven Insights

    • Privacy-Centric Approaches to Patient-Centred Care

  15. Conclusion and Recommendations

    • Summarizing Key Takeaways

    • Recommendations for Secure AI Implementation

    • Road Ahead for AI in Healthcare

Submission Guidelines

All papers must be original and not simultaneously submitted to another book, journal, or conference.

Kindly note the following when submitting your book chapter:

  • Submit an initial proposal through the EquinOCS system, including the chapter title and the problem/purpose statement explaining the proposed chapter:



  • The length of the chapter should be 6,000 - 8,000 words.

  • Paper should be formatted as per the template provided. 

  • Ensure that the paper adheres to Springer’s book chapter formatting guidelines. All submissions should be made in Word format only (.DOCX):

  • All papers will be checked for plagiarism.

  • The chapter template is given below:

       Download Word Template


       Sample Book Chapter


Feel free to email us if you have questions or want feedback on your proposed research question (,,, You may also contact us via professional social media at:

Important Dates

Expression of Interest                -    April 30, 2024

Draft Chapter Due Date             -    June 30, 2024 

Peer Review Period                   -    July 1 - August 30, 2024

Finalized Chapter Due               -    September 30, 2024

Final Acceptance Notification    -    October 30, 2024

Book Publication Date               -    December 2024

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