Digital Twins
For Superior Clinical Decision Making
1st Edition
Published on 21. August 2025
Book
Hardback
134 pages
978-1-032-78035-1 (ISBN)
Description
This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient's electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.
Together with cutting-edge technologies, the authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications and the numerous benefits offered. Artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL) algorithms, are discussed in the context of digital twins in healthcare applications. By looking at digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors identify critical success issues, including co-design and research, for the design, development, and deployment of suitable digital twins.
This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health.
Together with cutting-edge technologies, the authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications and the numerous benefits offered. Artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL) algorithms, are discussed in the context of digital twins in healthcare applications. By looking at digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors identify critical success issues, including co-design and research, for the design, development, and deployment of suitable digital twins.
This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health.
Reviews / Votes
"For decades, the digital representation of objects and entities has been a crucial field of research in computer science as well as application domains. Dynamic technological progress boosts the relevance by moving from modelling and instantiation towards realistically portrayed digital twins, e.g., by big data and artificial intelligence. Actually, the highest prospective impact of digital twins is anticipated in business and healthcare. Digital twins of products, machines, facilities and human beings will revolutionize the way we create value, for businesses as well as particularly for health providers and patients. This book is an eye-opener to that future."Freimut Bodendorf, Professor, Friedrich Alexander University, Erlangen, Nuremberg, Germany
"Digital twins are increasingly relevant in the age of personalized medicine. Exploration of the effects of medicines avoids the potential of debilitating side effects and promotes individual health. This book can be expected to have a major impact."
Doug Vogel, PhD, AIS and AIDH Fellow
"This book could not have come at a more opportune time. As providers struggle to raise clinical quality, lower costs, and accomplish this with fewer human resources, technology can help achieve this. Although NASA launched the digital twin effort, the time has come for it to land in healthcare."
Duane F. Wisk, DO, MPH, FACOEM, Adjunct Assistant Professor, Warren Alpert School of Medicine, Brown University
"Digital Twins offers an innovative approach to modern healthcare delivery, where timely decision-making depends on numerous data sources, many of which are available in real time. This book provides a comprehensive overview of Digital Twin applications in healthcare, offering valuable insights for researchers, practitioners, and decision-makers interested in harnessing this technology to enhance healthcare delivery."
Wenny Rahayu, PhD, Professor and Dean of the School of Computing, Engineering and Mathematical Sciences, La Trobe University
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Postgraduate, Professional Practice & Development, Professional Reference, Professional Training, and Undergraduate Advanced
Illustrations
7 s/w Abbildungen, 7 s/w Zeichnungen, 3 s/w Tabellen
3 Tables, black and white; 7 Line drawings, black and white; 7 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 13 mm
Weight
396 gr
ISBN-13
978-1-032-78035-1 (9781032780351)
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
Nilmini Wickramasinghe | Nalika Ulapane | Amir Andargoli
Digital Twins
For Superior Clinical Decision Making
E-Book
08/2025
1st Edition
CRC Press
€55.49
Available for download
Nilmini Wickramasinghe | Nalika Ulapane | Amir Andargoli
Digital Twins
For Superior Clinical Decision Making
E-Book
08/2025
1st Edition
CRC Press
€55.49
Available for download
Nilmini Wickramasinghe | Nalika Ulapane | Amir Andargoli
Digital Twins
For Superior Clinical Decision Making
Book
08/2025
1st Edition
CRC Press
€73.80
Shipment within 10-20 days
Persons
Nilmini Wickramasinghe is the Optus Chair and Professor of Digital Health at La Trobe University. She has been actively researching and teaching within the health informatics/digital health domain. In 2020, she was awarded an Alexander von Humboldt award for her outstanding contribution to digital health.
Nalika Ulapane is a researcher contributing to the design, development, and assessment of digital health solutions. He brings mathematical modelling, engineering systems design, and design science research principles to solve problems in complex systems like the healthcare sector.
Amir Andargoli focuses primarily on digitalization and digital transformation within the healthcare sector. He draws upon principles from information systems and management to conduct his research, which has resulted in publications in peer-reviewed journals and international symposiums.
Nalika Ulapane is a researcher contributing to the design, development, and assessment of digital health solutions. He brings mathematical modelling, engineering systems design, and design science research principles to solve problems in complex systems like the healthcare sector.
Amir Andargoli focuses primarily on digitalization and digital transformation within the healthcare sector. He draws upon principles from information systems and management to conduct his research, which has resulted in publications in peer-reviewed journals and international symposiums.
Content
Part I: The Why of Digital Twins/Why Now. 1. Decision-Making in Healthcare and the Rise of Technology and the Impact of the Digital Transformation. 2. Digital Twins in Other Industries. 3. The Case for Digital Twins for Healthcare. Part II: The What of Digital Twins. 4. From Algorithms to Outcomes: Leveraging Machine Learning Clustering Techniques for Enhanced Clinical Decision Support. 5. Clinical Decision Support through Federated Learning and Blockchain. 6. From Algorithms to Outcomes: Leveraging Machine Learning Classification Techniques for Enhanced Clinical Decision Support. 7. From Perceptron to Liquid Neural Networks: The Evolution of Neural Networks and Their Role in Black Box Modelling for Digital Twins in Healthcare. Part III: The How of Digital Twins. 8. Digital Twins and Clinical Decision-Making. 9. Application of Digital Twins in Healthcare Processes. 10. The Impact of Blockchain and Digital Twins in the Pharmaceutical Industry.