BRIDGING ARTIFICIAL AND HUMAN INTELLIGENCEIMPLEMENTATION STRATEGIES AND CASE STUDIES IN HEALTHCARE (HB 2026)
Language: English
Published by SPRINGER NP EXCLUSIVE(CBS), 2026
- Hardcover
- New
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Seller Inventory # CS 9783032119377
- Title
- BRIDGING ARTIFICIAL AND HUMAN INTELLIGENCEIMPLEMENTATION STRATEGIES AND CASE STUDIES IN HEALTHCARE (HB 2026)
- Author
- HABNER U H
- Publisher
- SPRINGER NP EXCLUSIVE(CBS)
- Publication year
- 2026
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3032119375
- ISBN 13
- 9783032119377
- Edition
- International Edition
This book reveals how human and artificial intelligence (AI) can be blended to augment the capacities of healthcare professionals, thereby enabling them to design their clinical work more effectively and efficiently. It provides a comprehensive overview about where human intelligence and AI overlap and differ, and how they can mutually enhance healthcare outcomes. Case studies illustrate the added value of AI for healthcare providers, patients and caregivers, and provides organizational strategies for implementing change and facilitating usability. It represents a valuable resource, providing insights into implementation strategies, as well as the ethical and legal frameworks relevant to AI integration.
Bridging Artificial and Human Intelligence addresses relevant topics impacting the healthcare ecosystem and workforce, serving as a pathway to understanding the complex relationship between the medical use of AI and human intelligence. Its primary target audience is graduate students in healthcare with a foundational understanding of biomedical and health informatics principles. Designed as a textbook, it enhances their understanding and competences in leveraging AI within healthcare contexts while emphasizing the importance of human oversight. Using this text, students will gain insights into the ethical and legal considerations surrounding AI implementation, highlighting the diverse roles humans play in this evolving landscape. A secondary audience comprises clinicians, professionals and decision-makers seeking to understand both the benefits and limitations of AI.
"Synopsis" may belong to another edition of this title.
About the Author
Ursula H. Hübner is a professor of Medical and Health Informatics and Quantitative Methods. She is the founder of the Research Centre for Health and Social Informatics. Her main fields of research in health sciences informatics are located at the intersection of informatics and statistics/intelligent algorithms. This includes AI prediction models in medicine and nursing, regional learning healthcare system and continuity of care, health IT adoption research and workforce development.
Giovanni Rubeis is a professor of Biomedical Ethics. He conducts research on questions of the ethics of digitalisation in medicine, nursing and mental health, prenatal and reproductive medicine and genome editing. His research focuses on the ethical aspects of digital applications in individualised medicine, with a particular focus on applications of artificial intelligence in medicine and care. Of particular interest is the concept of epistemic justice, i.e. the inclusion of groups that are underrepresented by epistemic practices in the context of Big Data and AI.
Marion J. Ball holds the Raj and Indra Nooyi Endowed Distinguished Chair in Bioengineering, University of Texas at Arlington, College of Nursing and Health Innovation. She serves as the Founding Executive Director of the Center for Innovation in Health Informatics (CIHI). A member of the National Academy of Medicine (NAM) and the American Academy of Nursing (AAN), she is a pioneer in Informatics in Nursing and Medicine. She is the author/editor of 40 books and more than 375 articles in the field of Health Informatics.
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