ReviewDigital health
Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Digital Twins and Health Care: an Umbrella Review.Journal of medical systems · 2025Pooled it
- Narrative Review of Digital Twins in the Health Domain: Development, Application, and Evidence Consolidation.Medical sciences (Basel, Switzerland) · 2026Review
- Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis.Journal of medical Internet research · 2026Review
- A comparative study on the credibility of ischemic stroke treatment information on social media platforms: evidence from Weibo and REDnote.Frontiers in digital health · 2026Article
- Research topic detection in scientific articles using a hybrid BERT integrated telescopic vector tree model with emperor penguin enhanced NSGA II optimization.Scientific reports · 2025Article
- Mapping interconnectivity of digital twin healthcare research themes through structural topic modeling.Scientific reports · 2025Article
- Digital twin manifesto for the pathology laboratory.Diagnostic pathology · 2025Article
- Digital Twins for Clinical and Operational Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Topic identification and content analysis of internet medical policies under the background of Healthy China 2030.Health research policy and systems · 2024Article
- The effectiveness of digital twins in promoting precision health across the entire population: a systematic review.NPJ digital medicine · 2024Review
- Evaluating remote healthcare practices: Experiences and recommendations of healthcare professionals on smart applications.Digital healthArticle
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 at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly. Methods: A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review. Results: A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement." Conclusions: This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.
Indexed as
Identifiers
What 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.