Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review Publisher



Zarei A ; Gholamzadeh M ; Asadi F
Source: International Journal of Telemedicine and Applications Published:2026

Abstract

Background: Given that chronic diseases account for a considerable proportion of preventable deaths globally, the adoption of innovative technologies for disease management and prevention is crucial. Digital twins (DTs), representing one of the most advanced technological solutions, enable real-time simulation and monitoring of chronic disease progression, facilitating personalized treatment strategies and early intervention. This systematic review examines current research on DT applications in chronic disease management to evaluate their potential impact. Methods: A systematic search was conducted in four databases including PubMed, Scopus, Web of Science, and IEEE from inception to the date of the last search. The research question was formulated using PICO framework. Next, all articles were screened following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to select eligible articles based on inclusion criteria. The extracted information was analyzed to determine the main applications, domains, and employed technologies using quantitative and qualitative techniques. Results: Out of 298 citations, 20 studies met our inclusion criteria after duplicate removal and screening. Most studies (45%, n = 10) were published between 2023 and 2024, indicating an increasing trend in this area. Geographically, the United States contributed the most studies (25%, n = 5), followed by Switzerland (15%, n = 3). Our analysis revealed that primary applications of DT in chronic disease management included medical training and education (65%, n = 13), personalized medicine and patient care (45%, n = 9), and drug discovery and clinical trials (35%, n = 7). Target groups comprised clinicians (42.11%), patients (31.58%), and medical students (15.79%). Key enabling technologies in this subject were data analytics (65%), artificial intelligence and machine learning (60%), computational physiological modeling (30%), and IoT sensors (25%). Conclusions: Our findings demonstrate that DT technology has evolved from theoretical models to integrated clinical applications, with the potential to revolutionize healthcare through personalized medicine, continuous monitoring, and AI-driven decision support. Copyright © 2026 Amirhossein Zarei et al. International Journal of Telemedicine and Applications published by John Wiley & Sons Ltd.