Enhancing Healthcare Informatics with Transparent and Explainable AI

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

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 Copy APA

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

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

1. AI Meets Nussbaum: Ethics for Smarter Healthcare — Barbara Gabriella Renzi and Giulio Napolitano
2. AI-Driven Decision-Making in Clinical Settings — Tyler Pratt, Ruiling Guo, Christopher A. Partridge, and Christian H. Fredericksen
3. Decision-Making Transparency Beyond Clinical Settings: Explainable AI Innovation in Telehealth and the Role of Social and Developmental Factors in AI Adoption — Virginia Gunn, Philip Eappen, and Theoneste Manishimwe
4. Explainable Multimodal Systems in Electronic Health Records and Predictive Analytics — Prerna Mishra and Tausif Diwan
5. Personalization in Healthcare Using AI — Pushpendra Kumar Verma, Gaurav Kumar, Paresh Pathak, and Shubham Kumar Sharma
6. A Systematic Review of EEG Analysis for the Identification and Classification of Harmful Brain Activity — Pillaram Manoj Sanjay and S Sharmila Devi
7. AI for Monitoring and Wearable Devices — Mitra Tithi Dey, Suman Patra, and Sucharita Mitra
8. Explainable AI for Mental Health in Healthcare Workers — Monika Srivastava
9. Artificial Intelligence in India’s Healthcare Revolution: Transforming Diagnostics and Personalized Care Through Collaboration — Jyoti Singh and Rajlaxmi Srivastava
10. Artificial Intelligence for Health-Monitoring and Wearable Devices — Wasswa Shafik
11. AI for Mental Health Care: Applications, Promise, Pitfalls, and Strategies to Build Patient and Health Worker Trust — Kathleen Nash, Lauren O’Donnell, Hikmat Singh Brar, Philip Eappen, and Virginia Gunn

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).

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

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

@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}
}