ReviewDigital health
A quarter-century of synthetic data in healthcare: Unveiling trends with structural topic modeling.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rethinking Trust in Synthetic Health Data: Lessons From 7 European Research Initiatives.Journal of medical Internet research · 2026Article
- Synthetic data in rheumatology: a systematic literature review.Rheumatology advances in practice · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To systematically map the research landscape of synthetic data in healthcare between 2000 and 2024, revealing prevalent topics and tracking their evolution over time and across geographic locations. Methods: We applied structural topic modeling (STM) to map this landscape, identifying prevalent topics and their evolution over time and geography. PubMed articles from 2000 to 2024 with "synthetic data," "artificial data," or "simulated data" in the title/abstract were analyzed. Texts were preprocessed (lowercasing, stopword removal, stemming), and STM was run with year and continent as covariates. The optimal number of topics (K = 10) was selected based on held-out likelihood and interpretability. Topic trends and correlations were analyzed using stacked area charts and network analysis. Results: Among 7533 articles, a 20-fold growth in publications was observed. North America (48.1%) and Europe (31.8%) dominated early research, while Asia's share rose from 4.7% to 24.1%. Topics grouped into four themes: Biomedical Imaging & Signal Processing (21.1%), Synthetic Data Applications (20.7%), Computational & Statistical Methods (34.3%), and Genomics & Molecular Biology (23.9%). Initially prominent topics such as "Bayesian Modeling" (23.1%-10.8%) and "Statistical Bias & Missing Data" (21.9%-7.1%) declined, while "Synthetic Data Generation" (2.7%-23.0%), and "Disease Modeling and Public Health" (3.5%-14.3%) grew significantly. Conclusion: Synthetic data research in healthcare is expanding, with shifting regional contributions and evolving topic focus. Realizing its potential requires cross-disciplinary collaboration, bias mitigation, and equitable partnerships.
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.