Edited book · 2026
AI in Healthcare: A Transparent Approach to Informatics
Chapman and Hall/CRC, 222 pp. · Published
Summary
Eappen et al. (2026) edited a volume of 11 chapters in four parts that covers how artificial intelligence can be introduced into health systems transparently and accountably across informatics, patient-centred care and clinical applications. 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. Chapter evidence and methods vary, and conclusions belong to the individual chapter authors rather than to the book as a whole.
Practical implication. Academics, practitioners and policymakers can use the volume as evidence-informed guidance on healthcare AI that covers the technology, its ethical and regulatory constraints, and its human impact in one place.
- 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
The summary, key coverage points, methods, implications and relevance notes on this page 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.
What the book covers
- Part I examines transparency and accountability: the opening chapter on implementing healthcare AI transparently, algorithmic scheduling and its consequences for employment conditions in healthcare, and the ethical implications of AI in healthcare.
- Part II covers transformative technologies for patient-centred care, adaptive technologies for clinical decision-making skills, and AI-enabled bidirectional device–provider communication.
- Part III presents AI applications in practice: disease preparedness and personalisation in healthcare.
- Part IV presents advanced clinical applications: deep learning for abdominal trauma diagnosis, a transparent deep neural network for ATC drug-class prediction, and wearable IoT for patient monitoring, chronic care and independent living.
Source: Eappen et al. (2026), Chapman and Hall/CRC, 222 pp. DOI: 10.1201/9781003624875
The book in detail
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
| Type | Edited book, 1st edition, 222 pages, 15 B/W illustrations |
|---|---|
| Editors | Philip Eappen, Virginia Gunn, Dimitrios Zikos, Narasimha Rao Vajjhala |
| Structure | Four parts: transparency and accountability; transformative technologies and patient-centred care; AI applications in practice; advanced AI technologies and clinical applications |
| Chapters | 11 |
| Audience | Academics, practitioners and policymakers in healthcare management, information systems management, healthcare policy and public health |
| Editors’ own chapter | Chapter 1, The Rise of AI in Healthcare: A Transparent Approach to Implementation (Eappen, Gunn, Zikos & Vajjhala) |
| Citation | Eappen et al. (2026) · DOI 10.1201/9781003624875 |
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
- Part I: Transparency and Accountability in Healthcare AI
- The Rise of AI in Healthcare: A Transparent Approach to Implementation
- Algorithmic Scheduling Raises Questions of Transparency, Accountability, and the Future of Employment Conditions in Healthcare
- Ethical Implications of Artificial Intelligence in Healthcare
- Part II: Transformative Technologies and Patient-Centered Care
- Transformative Technology in Healthcare: The Role of Emerging Technologies in Patient-Centered Care
- The Impact of Adaptive Technologies on Strengthening Clinical Decision-Making Skills
- AI-Enabled Bidirectional Healthcare: Advancing Patient Outcomes Through Intelligent Device-Provider Communication Systems
- Part III: AI Applications in Healthcare Practice
- Integrating Artificial Intelligence in Disease Preparedness
- Personalization in Healthcare Using AI
- Part IV: Advanced AI Technologies and Clinical Applications
- Harnessing Deep Learning for Swift Abdominal Trauma Diagnosis, Injury Assessment, and Streamlined Patient Care
- Transparent-ATC-DNN: Deep Neural Networks–Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis
- Wearable IoT in Healthcare: Transforming Patient Monitoring, Chronic Care, and Independent Living
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.
Practical implications
- Academics, practitioners and policymakers in healthcare management, information systems management, healthcare policy and public health can find guidance on AI technology, its ethical and regulatory constraints, and its human impact in one place.
- Researchers citing the volume should note that chapter evidence and methods vary and that conclusions belong to the individual chapter authors rather than to the book as a whole.
- Researchers and practitioners looking for applied chapters on clinical deep learning and wearable health monitoring can find them in Part IV, which covers abdominal trauma diagnosis, ATC drug-class prediction, and wearable IoT for patient monitoring.
- Policymakers and practitioners concerned with transparency and accountability in healthcare AI can turn to Part I, which examines transparent implementation, algorithmic scheduling and its consequences for employment conditions in healthcare, and the ethical implications of AI.
Drawn from Eappen et al. (2026), Chapman and Hall/CRC, 222 pp.
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
- APA 7
- 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
- MLA 9
- Eappen, Philip, et al., editors. AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC, 2026. https://doi.org/10.1201/9781003624875.
- IEEE
- P. Eappen, V. Gunn, D. Zikos, and N. R. Vajjhala, Eds., AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC, 2026, doi: 10.1201/9781003624875.
BibTeX
@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}
}RIS
TY - EDBOOK TI - AI in Healthcare: A Transparent Approach to Informatics ED - Eappen, Philip ED - Gunn, Virginia ED - Zikos, Dimitrios ED - Vajjhala, Narasimha Rao ET - 1st PY - 2026 DA - 2026/04/27 PB - Chapman and Hall/CRC SN - 9781003624875 DO - 10.1201/9781003624875 UR - https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1201/9781003624875
- Publisher’s page: https://www.routledge.com/AI-in-Healthcare-A-Transparent-Approach-to-Informatics/Eappen-Gunn-Zikos-Vajjhala/p/book/9781041036876
- This page as text: https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/
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