ReviewDigital health
Applications, technical foundations and challenges of digital twins for chronic disease management: A scoping review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.