Digital Twins and Healthcare: Trends, Techniques, and Challenges
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Digital Twins and Healthcare: Trends, Techniques, and Challenges

Loveleen Gaur (Amity University, India) and Noor Zaman Jhanjhi (Taylor's University, Malaysia)
Projected Release Date: November, 2022|Copyright: © 2023 |Pages: 335
DOI: 10.4018/978-1-6684-5925-6
ISBN13: 9781668459256|ISBN10: 1668459256|EISBN13: 9781668459263
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Description & Coverage
Description:

The healthcare industry is starting to adopt digital twins to improve personalized medicine, healthcare organization performance, and new medicine and devices. These digital twins can create useful models based on information from wearable devices, omics, and patient records to connect the dots across processes that span patients, doctors, and healthcare organizations as well as drug and device manufacturers. Digital twins are digital representations of human physiology built on computer models. The use of digital twins in healthcare is revolutionizing clinical processes and hospital management by enhancing medical care with digital tracking and advancing modelling of the human body. These tools are of great help to researchers in studying diseases, new drugs, and medical devices.

Digital Twins and Healthcare: Trends, Techniques, and Challenges facilitates the advancement and knowledge dissemination in methodologies and applications of digital twins in the healthcare and medicine fields. This book raises interest and awareness of the uses of digital twins in healthcare in the research community. Covering topics such as deep neural network, edge computing, and transfer learning method, this premier reference source is an essential resource for hospital administrators, pharmacists, medical professionals, IT consultants, students and educators of higher education, librarians, and researchers.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence
  • Bibliometric Analysis
  • Deep Neural Network
  • Diagnosis and Prognosis
  • Digital Twins
  • Edge Computing
  • Geospatial Information
  • Healthcare Data Analytics
  • Healthcare Multimedia
  • High-Resolution Computed Tomography (HRCT)
  • Transfer Learning Method
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