# AI in Healthcare: A Transparent Approach to Informatics

**Authors:** Philip Eappen (Cape Breton University, Sydney, Nova Scotia, Canada) — ORCID 0000-0002-8120-8449; Virginia Gunn — ORCID 0000-0003-3104-9539; Dimitrios Zikos — ORCID 0000-0002-2951-4350; Narasimha Rao Vajjhala (Department of Computer Science, American University in Bulgaria, Blagoevgrad, Bulgaria) — ORCID 0000-0002-8260-2392
**Type:** Edited book
**Source:** Chapman and Hall/CRC, 222 pp.
**Published:** 2026-04-27
**DOI:** https://doi.org/10.1201/9781003624875
**Canonical page:** https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/
**Indexing:** Edited book · Scopus
**Methodology:** 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.

## Research summary

- **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

## Summary in detail

**Question.** 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?

**Scope.** 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 it matters.** 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.

## What the book covers

1. 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.
2. Part II covers transformative technologies for patient-centred care, adaptive technologies for clinical decision-making skills, and AI-enabled bidirectional device–provider communication.
3. Part III presents AI applications in practice: disease preparedness and personalisation in healthcare.
4. 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.

## Book at a glance

| Item | Detail |
|---|---|
| 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) |

## 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.

## Contents

**Part I: Transparency and Accountability in Healthcare AI**
- The Rise of AI in Healthcare: A Transparent Approach to Implementation — Philip Eappen, Virginia Gunn, Dimitrios Zikos, and Narasimha Rao Vajjhala
- 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
- Ethical Implications of Artificial Intelligence in Healthcare — Christopher Partridge and Ruiling Guo
**Part II: Transformative Technologies and Patient-Centered Care**
- Transformative Technology in Healthcare: The Role of Emerging Technologies in Patient-Centered Care — Zainab Almukhtar and Philip Eappen
- The Impact of Adaptive Technologies on Strengthening Clinical Decision-Making Skills — Vismaya Annie Vinod and Philip Eappen
- AI-Enabled Bidirectional Healthcare: Advancing Patient Outcomes Through Intelligent Device-Provider Communication Systems — Ranney D.J. and Edward A.A.
**Part III: AI Applications in Healthcare Practice**
- Integrating Artificial Intelligence in Disease Preparedness — Wasswa Shafik
- Personalization in Healthcare Using AI — Ekta Bhaggi, Lalit Banger, and Simran Kaur Sandal
**Part IV: Advanced AI Technologies and Clinical Applications**
- 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
- Transparent-ATC-DNN: Deep Neural Networks–Based ATC Drug Class Prediction Using 17 Molecular Properties and Transparency Analysis — Anushka Chaurasia, Deepak Kumar, and Yogita
- Wearable IoT in Healthcare: Transforming Patient Monitoring, Chronic Care, and Independent Living — Krishnendhu Manoharan, Aswathi Bindhu Jawahar, and Philip Eappen

## 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.

## 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.

## 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

```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}
}
```