A Scoping Review of Digital Twins’ Applications and Challenges in Healthcare and Medicine
Abstract
Background: With the rapid growth of health data and advances in artificial intelligence and computational modeling, digital twin technology has emerged as a novel approach for simulating and analyzing complex systems. In medicine, the Medical Digital Twin (MDT) enables the creation of dynamic virtual representations of patients or clinical processes, allowing prediction of disease progression, monitoring of patient conditions, and support for clinical decision-making. This study aimed to systematically review the applications, opportunities, and challenges of MDT in healthcare using a systems-oriented perspective.
Methods: This study was conducted as a systematic review following the PRISMA guidelines. A comprehensive search was performed in PubMed, Web of Science, Scopus, and Google Scholar using the keywords “Digital Twin” and “Digital Medicine & Health.” A total of 465 records were initially identified. After removing duplicates and screening titles and abstracts, full texts were assessed according to inclusion criteria, including relevance to the research topic, publication in English or Persian, and a focus on the application of digital twin technology in healthcare. Ultimately, 25 studies were included in the final analysis.
Results: The findings indicate that MDT serves as a platform for integrating multimodal clinical, genetic, and physiological data and can be applied across a wide range of medical domains. These include hospital management and optimization of clinical processes, design and evaluation of medical devices and surgical procedures, drug discovery and development, personalized medicine, simulation of human physiology, and the design of clinical trials. The analysis also revealed that most studies primarily focus on technological and clinical applications, while comparatively limited attention has been given to systemic dimensions such as the role of digital twins in the health workforce and health system governance. Furthermore, several challenges hinder the broader implementation of MDT, including the integration of heterogeneous data sources, interpretability and generalizability of artificial intelligence models, data security and privacy concerns, and the need for scalable computational infrastructures.
Conclusion: The effective development and implementation of medical digital twins require a systems-based approach that extends beyond technological advancement to include integrated data infrastructures, interoperability standards, and robust data governance frameworks. Strengthening interdisciplinary collaboration among clinicians, data scientists, engineers, and policymakers is essential for ensuring the safe and effective use of this technology. Addressing existing technical and infrastructural challenges could enable MDT to become a strategic tool for advancing predictive and personalized medicine and improving managerial decision-making within health systems.
