{
  "abstract": "Objective\n\nTo systematically review the methods, validation frameworks, applications and regulatory landscape of synthetic data generation in healthcare, with a specific focus on rheumatology.Methods\n\nWe 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).Results\n\nThe 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.Conclusion\n\nSynthetic 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.",
  "abstract_shown": true,
  "affiliations": [
    "Rheumatology Unit, Department of Precision and Regenerative Medicine-Ionian Area (DiMePRe-J), University of Bari \"Aldo Moro\", Bari, Italy.",
    "Division of Rheumatology, Department of Internal Medicine, Faculty of Medicine, Sakarya University, Sakarya, Turkey.",
    "Department of Rheumatology, Royal Wolverhampton NHS Trust, Wolverhampton, UK.",
    "Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.",
    "Francis Crick Institute, Camden, London, UK."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Vincenzo Venerito",
      "orcid": "0000-0002-2573-5930"
    },
    {
      "affs": [
        0
      ],
      "name": "Maria Morrone",
      "orcid": "0009-0001-2513-6583"
    },
    {
      "affs": [
        0
      ],
      "name": "Sergio Del Vescovo",
      "orcid": "0009-0003-8970-7351"
    },
    {
      "affs": [
        1
      ],
      "name": "Emre Bilgin",
      "orcid": "0000-0002-2260-4660"
    },
    {
      "affs": [
        2,
        3,
        4
      ],
      "name": "Latika Gupta",
      "orcid": "0000-0003-2753-2990"
    },
    {
      "affs": [
        0
      ],
      "name": "Giuseppe Lopalco",
      "orcid": "0000-0003-4524-9566"
    },
    {
      "affs": [
        0
      ],
      "name": "Florenzo Iannone",
      "orcid": "0000-0003-0474-5344"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cv5ep2337d30d.bib",
  "cited_by": 0,
  "doi": "10.1093/rap/rkag114",
  "fulltext_url": "https://europepmc.org/articles/PMC13682607",
  "id": "cv5ep2337d30d",
  "issns": [
    "2514-1775"
  ],
  "landing_url": "https://europepmc.org/article/MED/42903943",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214506743",
  "pdf_url": "https://europepmc.org/articles/PMC13682607?pdf=render",
  "pmcid": "PMC13682607",
  "pmid": "42903943",
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cv5ep2337d30d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42903943",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42903943"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Synthetic data in rheumatology: a systematic literature review.",
  "title_text": "Synthetic data in rheumatology: a systematic literature review.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:57Z",
  "url": "https://research.draconitic.com/paper/cv5ep2337d30d",
  "venue": "Rheumatology advances in practice",
  "versions": []
}