Enhancing Healthcare Informatics with Transparent and Explainable AI

Inbunden, Engelska, 2026

2 238 kr

Kommande

Beskrivning

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."

Produktinformation

Utgivningsdatum:2026-08-03
Mått:156 x 234 x undefined mm
Format:Inbunden
Språk:Engelska
Antal sidor:260
Förlag:Taylor & Francis Ltd
ISBN:9781032992969

Utforska kategorier

Organisation och ägarskap inom Ekonomi och Ledarskap
Epidemiologi och medicinsk statistik inom Medicin
Databaser inom Data och IT

Mer om författaren

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 implementationof 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.

Innehållsförteckning

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