Synthetic data in rheumatology: a systematic literature review.

Authors

Vincenzo Venerito, Maria Morrone, Sergio Del Vescovo, Emre Bilgin, Latika Gupta, Giuseppe Lopalco, Florenzo Iannone

Published in

Rheumatology advances in practice. Volume 10. Issue 4. Pages rkag114. Epub Sep 25, 2026.

Abstract

To systematically review the methods, validation frameworks, applications and regulatory landscape of synthetic data generation in healthcare, with a specific focus on rheumatology.
We searched PubMed/MEDLINE, Embase and arXiv (January 2016-January 2026). From 1099 database records and 7 expert-identified studies, 701 remained after removing 405 duplicates. Title and abstract screening excluded 353 records; full-text assessment of 348 articles excluded 120 (E1: n = 62; E2: n = 1; E3: n = 23; E4: n = 34), yielding 228 studies. Quality assessment of 13 rheumatology-specific studies using a 10-item checklist revealed 1 high-quality, 8 moderate-quality, and 4 low-quality studies, with systematic gaps in privacy evaluation (2/13) and reproducibility (1/13).
The 228 included studies encompassed six generation-method categories (generative adversarial networks, diffusion models, large language models, statistical methods, rule-based simulators and hybrid approaches) and five thematic categories (digital twins, privacy-preserving methods, reviews, validation frameworks and healthcare applications), detailed in the supplementary methods. Validation remained inconsistent, with only a minority evaluating fidelity, utility and privacy simultaneously. In rheumatology, 13 studies spanned rheumatoid arthritis (n = 3), osteoarthritis (n = 3), Sjogren's syndrome (n = 2), systemic sclerosis (n = 2), systemic lupus erythematosus (n = 2) and ankylosing spondylitis (n = 1). Only 2 of 13 formally evaluated privacy and only 1 shared code for reproducibility.
Synthetic data hold substantial promise for rheumatology, particularly for augmenting small cohorts in low-prevalence diseases, enabling privacy-preserving registry sharing and simulating clinical trial populations. Realizing this potential requires standardized validation, greater attention to privacy and reproducibility and regulatory clarity on synthetic health data.

PMID:
42903943
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.

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