Applications, technical foundations and challenges of digital twins for chronic disease management: A scoping review

Digital twins (DTs) have gradually demonstrated application potential in chronic disease management through the construction of individualized and continuously updated virtual representations of patients. Unlike traditional digital therapeutics, digital twins emphasize dynamic modeling, bidirectional data interaction, and forward-looking decision support. However, the specific application models and effects, technical foundations, and implementation challenges of digital twins in chronic disease management remain relatively scattered and require systematic review.

The purpose of this scoping review is to comprehensively analyze the concept, application effects, technical basis, and key challenges in the implementation process of digital twins in chronic disease management.

This study adheres to the methodological framework of Arksey and O'Malley's scoping review and is conducted in accordance with the PRISMA-ScR reporting guidelines. A systematic search was conducted in the PubMed, Science Direct, Web of Science, Embase, SinoMed, CNKI, and Wanfang databases from 2019 to December 10, 2025. The search strategy combined free terms and subject terms. The "PCC" principle was used to determine the inclusion criteria. The relevant literature was analyzed and discussed. The research results are presented in tabular and descriptive formats.

A total of 20 studies were included. Digital twins are applied mainly in areas such as diabetes, cardiovascular diseases, obesity, cancer care, and functional monitoring related to aging. Most systems are based on data-driven models, physics-based models, or hybrid models and support predictive modeling, clinical decision support, and lifestyle or behavior intervention. In different disease scenarios, digital twin systems are positively correlated with glucose control, blood pressure management, risk monitoring, and improvement in personalized care. However, there is significant heterogeneity in modeling approaches, evaluation indicators, and research designs among the studies. Clinical integration predominantly adopts open-loop and human-in-the-loop intervention modes, whereas closed-loop operations are completed only via in silico simulations.

As emerging paradigms, DTs hold significant potential for enhancing the accuracy and individualization of chronic disease management. Future research should focus on long-term real-world effect evaluation, develop standardized and flexible technical architectures, strengthen collaborative design and fair-oriented governance strategies, and pay particular attention to the coverage of elderly people, patients with multiple chronic diseases, and those with low digital literacy to promote the responsible and large-scale application of digital twins in chronic disease management.