SynthesisJournal of medical systems2025
Digital Twins and Health Care: an Umbrella Review.
Synthesis in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Applications of digital twin technology in cardiometabolic disease management: a scoping review.BMC medical informatics and decision making · 2026Article
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (Basel, Switzerland) · 2026Review
- Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.BMC medical informatics and decision making · 2026Article
- Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management.Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technological advancements are enhancing healthcare, with digital twin (DT) technology emerging as a key tool for personalized and efficient care. This umbrella review systematically evaluates the literature on DT applications in healthcare, focusing on their effectiveness, challenges, and potential to substantially improve patient care.An umbrella review was conducted following the Joanna Briggs Institute (JBI) manual for evidence synthesis and the PRISMA guidelines. A comprehensive literature search was performed across multiple databases, including PubMed, Scopus, Web of Science, and IEEE Xplore, targeting systematic reviews published up to July 2024. The inclusion criteria focused on systematic reviews and meta-analyses related to the usage of DT technologies in healthcare settings.The review identified a considerable number of systematic reviews that highlight the role of DTs in various domains of healthcare, including personalized medicine, predictive maintenance of medical equipment, and healthcare system optimization. Key themes included the integration of real-time data and predictive modeling, which enhance chronic disease management and surgical planning. However, barriers to implementation were noted, including data privacy concerns, validation issues, and high costs.DT technology has the potential to enhance healthcare delivery by enabling personalized treatment and improving operational efficiencies. However, addressing ethical challenges, particularly concerning data privacy and security, is crucial for the successful integration of DTs in clinical practice. This umbrella review underscores the need for continued research to overcome these challenges and facilitate the widespread adoption of DT technologies in healthcare.
Indexed as
Identifiers
41420670What OpenQuestion 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 OpenQuestion graph.