Meijer, C.; Uh, H.-W.; el Bouhaddani, S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J. Pers. Med.2023, 13, 1522.
Meijer, C.; Uh, H.-W.; el Bouhaddani, S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J. Pers. Med. 2023, 13, 1522.
Meijer, C.; Uh, H.-W.; el Bouhaddani, S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J. Pers. Med.2023, 13, 1522.
Meijer, C.; Uh, H.-W.; el Bouhaddani, S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J. Pers. Med. 2023, 13, 1522.
Abstract
One of the most promising advancements in healthcare is the application of digital twin technology. Digital twins are virtual replicas of real-world human patients and can be used for monitoring, and diagnosis, and as a tool to design treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. This review article delves into the various data sources and methodologies that contribute to the construction of digital twins. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.
Keywords
virtual twins; personalized medicine; precision medicine; digital twin methodology; multi-modal data sources; AI; data integration
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
