Enhancing Healthcare Informatics with Transparent and Explainable AI
1st Edition
Will be published approx. on 3. August 2026
Book
Hardback
260 pages
978-1-032-99296-9 (ISBN)
Description
Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.
This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.
Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.
At the threshold of AI's deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought-it is a fundamental requirement for responsible AI deployment in healthcare.
This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.
Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.
At the threshold of AI's deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought-it is a fundamental requirement for responsible AI deployment in healthcare.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Practice & Development and Professional Reference
Illustrations
10 farbige Zeichnungen, 10 farbige Abbildungen
16 Line drawings, color; 16 Illustrations, color
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-032-99296-9 (9781032992969)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Philip Eappen | Narasimha Rao Vajjhala | Ruiling Guo
Enhancing Healthcare Informatics with Transparent and Explainable AI
Book
approx. 08/2026
1st Edition
Auerbach
€74.50
Not yet published
Philip Eappen | Narasimha Rao Vajjhala | Ruiling Guo
Enhancing Healthcare Informatics with Transparent and Explainable AI
E-Book
approx. 08/2026
1st Edition
Auerbach
€78.99
Not yet available
Philip Eappen | Narasimha Rao Vajjhala | Ruiling Guo
Enhancing Healthcare Informatics with Transparent and Explainable AI
E-Book
approx. 08/2026
1st Edition
Auerbach
€78.99
Not yet available
Persons
Dr. Philip Eappen is a healthcare executive, academic, and researcher currently serving as Assistant Professor in the Department of Professional Studies, School of Nursing; Adjunct Faculty in Healthcare Management, Shannon School of Business at Cape Breton University; and Associate Scientist with the Maritime SPOR SUPPORT Unit. With over a decade of experience spanning hospital operations, healthcare education, and strategic leadership, he has consistently advanced innovation and excellence across diverse healthcare systems in Canada, the United States, and internationally.
Dr. Narasimha Rao Vajjhala is a distinguished academic and researcher currently serving as Professor and Chair of the Department of Computer Science at the American University in Bulgaria (AUBG). With over two decades of experience in higher education, Dr. Vajjhala has held senior academic leadership positions, including Dean of the Faculty of Engineering and Architecture at the University of New York Tirana (UNYT), Albania, and Chair of Computer Science and Software Engineering programs at the American University of Nigeria (AUN).
Dr. Ruiling Guo is a Professor of Healthcare Administration at Idaho State University's College of Business, where she teaches both graduate and undergraduate courses in healthcare administration. She also holds a graduate faculty appointment in Idaho State University's Graduate School, serving on dissertation and thesis committees for doctoral and graduate students in medicine, health sciences, and health professions.
Dr. Lucy Shinners is an accomplished nurse, educator, and researcher with over two decades of experience bridging clinical care, academic leadership, and AI-driven innovation in healthcare. As Chief Operating Officer at Datarwe, she leads the co-design and validation of AI-enabled products that deliver safer, more patient-centered healthcare solutions.
Dr. Virginia Gunn is an Associate Professor in the School of Nursing at Cape Breton University and an Affiliate Researcher at the Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institute, Sweden. As a public health researcher with interdisciplinary training in health and social sciences, Dr. Gunn earned her doctoral and master's degrees from the Lawrence Bloomberg Faculty of Nursing and completed a Collaborative Doctoral Specialization in Global Health from the Dalla Lana School of Public Health, University of Toronto.
Dr. Narasimha Rao Vajjhala is a distinguished academic and researcher currently serving as Professor and Chair of the Department of Computer Science at the American University in Bulgaria (AUBG). With over two decades of experience in higher education, Dr. Vajjhala has held senior academic leadership positions, including Dean of the Faculty of Engineering and Architecture at the University of New York Tirana (UNYT), Albania, and Chair of Computer Science and Software Engineering programs at the American University of Nigeria (AUN).
Dr. Ruiling Guo is a Professor of Healthcare Administration at Idaho State University's College of Business, where she teaches both graduate and undergraduate courses in healthcare administration. She also holds a graduate faculty appointment in Idaho State University's Graduate School, serving on dissertation and thesis committees for doctoral and graduate students in medicine, health sciences, and health professions.
Dr. Lucy Shinners is an accomplished nurse, educator, and researcher with over two decades of experience bridging clinical care, academic leadership, and AI-driven innovation in healthcare. As Chief Operating Officer at Datarwe, she leads the co-design and validation of AI-enabled products that deliver safer, more patient-centered healthcare solutions.
Dr. Virginia Gunn is an Associate Professor in the School of Nursing at Cape Breton University and an Affiliate Researcher at the Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institute, Sweden. As a public health researcher with interdisciplinary training in health and social sciences, Dr. Gunn earned her doctoral and master's degrees from the Lawrence Bloomberg Faculty of Nursing and completed a Collaborative Doctoral Specialization in Global Health from the Dalla Lana School of Public Health, University of Toronto.
Content
1. AI Meets Nussbaum: Ethics for Smarter Healthcare 2. AI-Driven Decision-Making in Clinical Settings 3. Decision-Making Transparency Beyond Clinical Settings: Explainable AI Innovation in Telehealth and the Role of Social and Developmental Factors in AI Adoption 4. Explainable Multimodal Systems in Electronic Health Records and Predictive Analytics 5. Personalization in Healthcare Using AI 6. A Systematic Review of EEG Analysis for the Identification and Classification of Harmful Brain Activity 7. AI for Health-Monitoring and Wearable Devices 8. Explainable AI for Mental Health in Healthcare Workers 9. Artificial Intelligence in India's Healthcare Revolution: Transforming Diagnostics and Personalized Care Through Collaboration 10. Artificial Intelligence for Health Monitoring and Wearable Devices 11. AI for Mental Health Care: Applications, Promise, Pitfalls, and Strategies to Build Patient and Health-Worker Trust