Edited book · 2026
Enhancing Healthcare Informatics with Transparent and Explainable AI
Auerbach Publications (CRC Press), 280 pp. · Published
Summary
Eappen et al. (2026) edited a volume of 11 chapters by international contributors that covers explainable and transparent AI across clinical decision-making, telehealth, electronic health records, EEG analysis, wearable monitoring and mental health. The volume pairs ethical analysis with applied systems and strategies for building trust, and its chapters range from ethical and conceptual analysis to a systematic review of EEG analysis and applied explainable-AI system descriptions. As an edited collection, its evidence and methods vary by chapter.
Practical implication. For clinicians and patients, an AI recommendation about treatment must be explainable, and the book focuses on transparency and explainability as the condition for safe adoption.
- The Problem
- Clinicians, patients and health workers will not rely on AI in healthcare informatics unless its decisions are explainable and transparent.
- The Approach
- Edited volume of 11 chapters by international contributors; chapters range from ethical and conceptual analysis to systematic review (EEG analysis) and applied explainable-AI system descriptions.
- The Core Contribution
- The volume applies explainable and transparent AI across clinical decision-making, telehealth, electronic health records, EEG analysis, wearable monitoring and mental health, pairing ethical analysis with applied systems and strategies for building trust.
- The Citation
- Eappen, P., Vajjhala, N. R., Guo, R., Shinners, L., & Gunn, V. (Eds.). (2026). Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press). https://doi.org/10.1201/9781003604440
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
- Opens with an ethics chapter (AI and Nussbaum’s capabilities approach) and a chapter on AI-driven decision-making in clinical settings.
- Extends explainability beyond the clinic to telehealth, considering social and developmental factors in AI adoption, and to explainable multimodal systems in electronic health records and predictive analytics.
- Includes a systematic review of EEG analysis for identifying and classifying harmful brain activity, and chapters on AI for monitoring and wearable devices.
- Addresses mental health twice: explainable AI for the mental health of healthcare workers, and the applications, promise, pitfalls and trust-building strategies of AI in mental health care.
- Includes a regional chapter on AI in India’s healthcare transformation through collaboration.
Source: Eappen et al. (2026), Auerbach Publications (CRC Press), 280 pp. DOI: 10.1201/9781003604440
The book in detail
What question does this book address?
How can explainable and transparent AI methods be applied across healthcare informatics — clinical decision-making, telehealth, electronic health records, monitoring and mental health — so that clinicians, patients and health workers can trust them?
What does the book cover?
The volume collects eleven chapters spanning the ethics of AI in healthcare, AI-driven decision-making in clinical settings, explainable AI in telehealth and the social factors of adoption, explainable multimodal systems for electronic health records and predictive analytics, personalisation, EEG analysis for harmful brain activity, AI for monitoring and wearable devices, explainable AI for the mental health of healthcare workers, AI in India’s healthcare transformation, and strategies to build patient and health-worker trust in AI mental-health care.
Why does it matter?
Healthcare differs from other domains where AI has succeeded: a recommendation about treatment must be explainable to the clinician and the patient. The book focuses specifically on transparency and explainability as the condition for safe adoption.
Book at a glance
| Type | Edited book, 1st edition, 280 pages |
|---|---|
| Editors | Philip Eappen, Narasimha Rao Vajjhala, Ruiling Guo, Lucy Shinners, Virginia Gunn |
| Chapters | 11 |
| Themes | Ethics; clinical decision-making; telehealth; EHRs and predictive analytics; personalisation; EEG analysis; monitoring and wearables; mental health; national health-system transformation; trust |
| Publisher | Auerbach Publications, an imprint of CRC Press / Taylor & Francis |
| Citation | Eappen et al. (2026) · DOI 10.1201/9781003604440 |
Publisher’s description
Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a…
Only the opening of the publisher’s description is available in public metadata; see the publisher page for the full text.
Contents
- AI Meets Nussbaum: Ethics for Smarter Healthcare
- AI-Driven Decision-Making in Clinical Settings
- Decision-Making Transparency Beyond Clinical Settings: Explainable AI Innovation in Telehealth and the Role of Social and Developmental Factors in AI Adoption
- Explainable Multimodal Systems in Electronic Health Records and Predictive Analytics
- Personalization in Healthcare Using AI
- A Systematic Review of EEG Analysis for the Identification and Classification of Harmful Brain Activity
- AI for Monitoring and Wearable Devices
- Explainable AI for Mental Health in Healthcare Workers
- Artificial Intelligence in India’s Healthcare Revolution: Transforming Diagnostics and Personalized Care Through Collaboration
- Artificial Intelligence for Health-Monitoring and Wearable Devices
- AI for Mental Health Care: Applications, Promise, Pitfalls, and Strategies to Build Patient and Health Worker Trust
Scope and limitations
- An edited collection: evidence and methods vary by chapter. Only the eBook ISBN is registered with Crossref; the publisher lists the first-published date as 2026 (Crossref 25 June 2026; the chapter pages state 3 August 2026).
Practical implications
- Clinicians, patients and health workers will not rely on AI in healthcare informatics unless its decisions are explainable and transparent, which is the problem the volume addresses.
- Researchers working on explainable AI in health informatics will find explainability extended beyond the clinic to telehealth, considering social and developmental factors in AI adoption, and to explainable multimodal systems in electronic health records and predictive analytics.
- Researchers working on AI and mental health will find the topic addressed twice: explainable AI for the mental health of healthcare workers, and the applications, promise, pitfalls and trust-building strategies of AI in mental health care.
- Researchers citing the volume will find that evidence and methods vary by chapter, because it is an edited collection.
Drawn from Eappen et al. (2026), Auerbach Publications (CRC Press), 280 pp.
When this research may be relevant
This book may be relevant to researchers working on explainable AI (XAI) in medicine and health informatics, trustworthy clinical decision support, AI in telehealth and remote monitoring, AI and mental health, and ethics-led approaches to healthcare AI adoption.
Research topics addressed: explainable AI; healthcare informatics; transparency in clinical AI; AI-driven clinical decision-making; telehealth; electronic health records; predictive analytics; wearable devices; AI for mental health; EEG analysis; trust in healthcare AI; ethics of AI
How to cite
- APA 7
- Eappen, P., Vajjhala, N. R., Guo, R., Shinners, L., & Gunn, V. (Eds.). (2026). Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press). https://doi.org/10.1201/9781003604440
- MLA 9
- Eappen, Philip, et al., editors. Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press), 2026. https://doi.org/10.1201/9781003604440.
- IEEE
- P. Eappen, N. R. Vajjhala, R. Guo, L. Shinners, and V. Gunn, Eds., Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press), 2026, doi: 10.1201/9781003604440.
BibTeX
@book{eappen2026enhancing,
title = {Enhancing Healthcare Informatics with Transparent and Explainable AI},
editor = {Eappen, Philip and Vajjhala, Narasimha Rao and Guo, Ruiling and Shinners, Lucy and Gunn, Virginia},
isbn = {9781003604440},
edition = {1st},
year = {2026},
publisher = {Auerbach Publications (CRC Press)},
doi = {10.1201/9781003604440},
url = {https://doi.org/10.1201/9781003604440}
}RIS
TY - EDBOOK TI - Enhancing Healthcare Informatics with Transparent and Explainable AI ED - Eappen, Philip ED - Vajjhala, Narasimha Rao ED - Guo, Ruiling ED - Shinners, Lucy ED - Gunn, Virginia ET - 1st PY - 2026 DA - 2026/06/25 PB - Auerbach Publications (CRC Press) SN - 9781003604440 DO - 10.1201/9781003604440 UR - https://www.narasimharao.net/research/enhancing-healthcare-informatics-transparent-explainable-ai/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1201/9781003604440
- Publisher’s page: https://www.taylorfrancis.com/books/edit/10.1201/9781003604440/enhancing-healthcare-informatics-transparent-explainable-ai-philip-eappen-narasimha-rao-vajjhala-ruiling-guo-lucy-shinners-virginia-gunn
- This page as text: https://www.narasimharao.net/research/enhancing-healthcare-informatics-transparent-explainable-ai/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/enhancing-healthcare-informatics-transparent-explainable-ai/
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