ReviewDigital health
Digital twins in healthcare: A systematic review of current applications, frameworks, and future directions.
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This systematic review aims to evaluate current digital twin (DT) applications in healthcare, explore their technological foundations, and propose a roadmap for scalable, patient-centered implementation. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, a systematic search was conducted across Medline, Scopus, Web of Science, and EBSCO up to May 2025. Eligible studies included peer-reviewed research on DT applications in clinical or healthcare settings involving human or patient-related data. Methodological quality was assessed using appropriate Joanna Briggs Institute critical appraisal tools based on study design. The systematic review protocol was prospectively registered in Prospective Register of Systematic Reviews (registration number: CRD420251120304). Results: 26 studies were included, with most published between 2023 and 2025. DT applications spanned diagnostics, therapy optimization, physiological monitoring, and system-level modeling. Simulation-based designs dominated, often integrating artificial intelligence, internet of things, and machine learning. While several studies reported strong technical performance (e.g. up to 96.3% accuracy), real-world clinical integration was rare. Notable outcomes included better glycemic control, pain management, and disease progression prediction. Barriers included insufficient infrastructure detail, limited validation, and equity concerns. The roadmap highlights three enablers: privacy-preserving, validation pipelines, and interoperability. Conclusion: DTs offer transformative potential for predictive, personalized, and participatory healthcare. Realizing clinical impact requires bridging the translational gap and scaling personalization. This review outlines key strategies for interdisciplinary innovation and deployment of DTs in healthcare.
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What Socratic holds
Registered trials
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 Socratic graph.