AI in Healthcare: A Transparent Approach to Informatics

Research summary

The summary, key coverage points, methodology and relevance notes below are this website’s own description of the book, written from the published description and table of contents. The official publisher description and citation details are given further down.

The Problem

Health systems are adopting AI without shared practice for making it transparent and accountable across informatics, patient-centred care and clinical applications.

The Approach

Edited volume of 11 chapters (4 parts) by international contributors; chapter methods range from conceptual and ethical analysis to case studies and technical deep-learning evaluations.

The Core Contribution

The volume combines conceptual, ethical, case-based and technical chapters — from transparent implementation and algorithmic scheduling to deep learning for abdominal trauma diagnosis and transparent drug-class prediction — into a framework for transparent healthcare AI.

The Citation

Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (Eds.). (2026). AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875 Copy APA

What question does this book address?

How can artificial intelligence be introduced into health systems transparently and accountably, and what practical, ethical and regulatory considerations shape its use across informatics, patient-centred care and clinical applications?

What does the book cover?

The volume brings together eleven chapters in four parts — transparency and accountability in healthcare AI; transformative technologies and patient-centred care; AI applications in healthcare practice; and advanced AI technologies and clinical applications — combining conceptual analysis with case-based and technical chapters on algorithmic scheduling, ethics, adaptive clinical technologies, disease preparedness, personalisation, deep learning for abdominal trauma diagnosis, transparent drug-class prediction and wearable IoT.

Why does it matter?

AI adoption in healthcare is accelerating after the pandemic, and the editors set out to give academics, practitioners and policymakers evidence-informed guidance that covers the technology, its ethical and regulatory constraints, and its human impact in one place.

Book at a glance

Publisher’s description

This book addresses the urgent need for AI healthcare insights in a post-pandemic and swiftly progressing world in which the use of advanced health technologies is becoming the norm. It explores the rapidly evolving AI landscape affecting health systems and provides practical, evidence-informed guidance drawn from practice, theory, and research. Covering a wide range of topics—from digital health technologies to data analytics and interoperability—it offers a thorough examination of advancements in these areas, giving readers a comprehensive understanding of the health sector's transformation. The book goes beyond theory, showcasing practical applications of AI solutions, discussing both advantages and possible disadvantages. Real-world case studies and expert perspectives drawn from recent developments equip readers with actionable insights for implementation in various healthcare settings. With a holistic approach, the book explores not only technological aspects but also ethical considerations, regulatory challenges, and the human impact of digital transformation. This invaluable resource is essential for academics, practitioners, and policymakers in healthcare management, information systems management, healthcare policy, and public health.

Description as published by Chapman and Hall/CRC.

Contents

1. Part I: Transparency and Accountability in Healthcare AI
2. The Rise of AI in Healthcare: A Transparent Approach to Implementation — Philip Eappen, Virginia Gunn, Dimitrios Zikos, and Narasimha Rao Vajjhala
3. Algorithmic Scheduling Raises Questions of Transparency, Accountability, and the Future of Employment Conditions in Healthcare — Hilary Hall, Virginia Gunn, Karen Kennedy, Adam Elliott, Kelsey Morrison, and Matthew MacLeod
4. Ethical Implications of Artificial Intelligence in Healthcare — Christopher Partridge and Ruiling Guo
5. Part II: Transformative Technologies and Patient-Centered Care
6. Transformative Technology in Healthcare: The Role of Emerging Technologies in Patient-Centered Care — Zainab Almukhtar and Philip Eappen
7. The Impact of Adaptive Technologies on Strengthening Clinical Decision-Making Skills — Vismaya Annie Vinod and Philip Eappen
8. AI-Enabled Bidirectional Healthcare: Advancing Patient Outcomes Through Intelligent Device-Provider Communication Systems — Ranney D.J. and Edward A.A.
9. Part III: AI Applications in Healthcare Practice
10. Integrating Artificial Intelligence in Disease Preparedness — Wasswa Shafik
11. Personalization in Healthcare Using AI — Ekta Bhaggi, Lalit Banger, and Simran Kaur Sandal
12. Part IV: Advanced AI Technologies and Clinical Applications
13. Harnessing Deep Learning for Swift Abdominal Trauma Diagnosis, Injury Assessment, and Streamlined Patient Care — Rajendra Hegadi, Sudeepto Chatterjee, Prathamesh P. Pai, and Ashis J. Kalathil
14. Transparent-ATC-DNN: Deep Neural Networks–Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis — Anushka Chaurasia, Deepak Kumar, and Yogita
15. Wearable IoT in Healthcare: Transforming Patient Monitoring, Chronic Care, and Independent Living — Krishnendhu Manoharan, Aswathi Bindhu Jawahar, and Philip Eappen

Scope and limitations

• An edited collection: chapter evidence and methods vary, and conclusions belong to the individual chapter authors rather than to the book as a whole.

When this research may be relevant

This book may be relevant to researchers and practitioners looking for a multi-chapter treatment of trustworthy and transparent AI in healthcare, healthcare informatics after COVID-19, ethical and regulatory governance of clinical AI, and applied chapters on clinical deep learning and wearable health monitoring.

Research topics addressed: artificial intelligence in healthcare; healthcare informatics; transparency and accountability; ethics of healthcare AI; digital health technologies; data analytics and interoperability; patient-centred care; clinical decision-making; wearable IoT; deep learning in diagnosis; health policy

How to cite

Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (Eds.). (2026). AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875

@book{eappen2026ai,
  title = {AI in Healthcare: A Transparent Approach to Informatics},
  editor = {Eappen, Philip and Gunn, Virginia and Zikos, Dimitrios and Vajjhala, Narasimha Rao},
  isbn = {9781003624875},
  edition = {1st},
  year = {2026},
  publisher = {Chapman and Hall/CRC},
  doi = {10.1201/9781003624875},
  url = {https://doi.org/10.1201/9781003624875}
}