Enhancing Healthcare Informatics with Transparent and Explainable AI
Language: English
Published by Taylor & Francis Ltd (Sales) Aug 2026, 2026
- Softcover
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
US$ 144.93
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Add to basketItem description from seller
Neuware - 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.'.…
Seller Inventory # 9781032995007
- Title
- Enhancing Healthcare Informatics with Transparent and Explainable AI
- Author
- Ruiling Guo
- Publisher
- Taylor & Francis Ltd (Sales) Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1032995009
- ISBN 13
- 9781032995007
- Item weight
- 399 grams
- Dimensions
- 234x156x15 mm
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."
"Synopsis" may belong to another edition of this title.
About the Author
Philip Eappen is a tenured associate professor in the School of Nursing at Cape Breton University and serves as the Director of Research in Medicine at Dalhousie University’s Cape Breton Medical Campus, Canada. A registered nurse with a doctorate in healthcare administration and an MBA in healthcare management,
Dr. Eappen is a Certified Health Executive (CHE) and a Fellow candidate of the American College of Healthcare Executives. His academic and clinical leadership bridge frontline healthcare delivery, macro-level health systems, and cutting-edge informatics.
As an associate scientist with the Maritime SPOR SUPPORT Unit and a scientific editor for Elsevier, Dr. Eappen is heavily engaged in advancing health services research. His primary research focus centers on the ethical and transparent application of healthcare informatics in clinical workflows. Dr. Eappen also contributes to national and international health governance, serving on the boards of Myeloma Canada, the Aplastic Anemia and the Myelodysplasia Association of Canada, and the American College of Healthcare Executives.
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).
Ruiling Guo is 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 at Idaho State University’s Graduate School, serving on dissertation and thesis committees for doctoral and graduate students in medicine, health sciences, and health professions.
Lucy Shinners is an Indigenous socio-technical systems researcher whose work examines how artificial intelligence (AI) shapes the health workforce and the performance of emerging technologies. She is a critical care nurse with more than practice and 10 years in academia as a teacher and researcher.
Dr. Shinners has held academic leadership roles, including course coordinator of the Bachelor of Nursing program at Southern Cross University, and currently serves as research fellow at the Centre for Infection Prevention and Vascular Access, University of Queensland, Australia. Her extensive ICU nursing background provides grounded clinical insight into the design, evaluation, and implementation
of AI in healthcare.
Her research has a strong focus on culturally informed innovation, including the application of AI within Indigenous health contexts. She is the developer of the internationally adopted SHAIP tool, which is advancing how health systems understand and evaluate workforce perceptions of AI.
Virginia Gunn is an associate professor in the School of Nursing at Cape Breton University, Nova Scotia, Canada, and affiliate researcher with the Unit of Occupational Medicine, Institute of Environmental Medicine, Karolinska Institute, Sweden. She also holds an appointment as an associate scholar at the Johns Hopkins University–Universitat Pompeu Fabra Public Policy Center, Barcelona, Spain. Dr. Gunn earned her Ph.D. and MN from the University of Toronto’s Lawrence S. Bloomberg Faculty of Nursing and completed a Collaborative Doctoral Specialization in Global Health at the Dalla Lana School of Public Health, Toronto, Canada. Her research spans public health, policy, occupational health, and healthcare informatics, with a focus on how AI-enabled systems―particularly those influencing decision-making, work organization, and care delivery―shape equity, accountability, and trust. Drawing on her experience as a registered nurse across acute care, long-term care, and public health, she integrates frontline practice insights with interdisciplinary research to advance transparent and explainable AI in clinical and virtual care settings.
"About the title" may belong to another edition of this title.
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