A quarter-century of synthetic data in healthcare: Unveiling trends with structural topic modeling

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.

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.

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.

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.