Utilizing Digital Twin Technology for Personalized Healthcare Solutions

Abstract

Objective: Patient Digital Twins (PDT) represent a significant 
development in the field of personalized medicine. PDTs enhance 
and accelerate the traditionally slow and often imprecise approach of 
cancer treatment that relies on physicians' intuition by creating 
digital models that can simulate, monitor, and forecast health 
outcomes, making it simpler to identify tailored treatment options for 
individual patients. This review is designed with the primary goal of 
exploring how PDTs can enhance diagnostic precision, customize 
patient care, and address the challenges of implementation in clinical 
environments.
Methods: The databases utilized for performing an in-depth 
literature review included PubMed, IEEE Xplore, and Scopus. The 
purpose of this review is to analyze research published between 
January 2018 and August 2024 regarding the applications, 
advantages, and drawbacks of PDTs in clinical medicine.
Results: Nonetheless, it has been demonstrated that PDTs can 
enhance diagnostic capabilities by integrating data from various 
sources such as genomics, imaging, and biometrics. The introduction 
of AI and machine learning models in PDTs has notably improved
predictive abilities, allowing clinicians to assess the likelihood of 
disease progression and improve their capacity to offer personalized 
treatments for each patient. However, the widespread 
implementation of PATH will only occur once concerns regarding 
data privacy, algorithm transparency, and system integration are 
resolved.
Conclusion: PDTs have the potential to revolutionize clinical care by 
offering targeted, timely, and individualized treatments. However, 
the full capabilities of these technologies are still not fully realized in 
everyday medical practice because of various technical, ethical, and 
regulatory challenges.