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Screening used a \u201chuman-in-the-loop\u201d large language model workflow alongside independent manual review, achieving 100% sensitivity for included studies. Unlike prior reviews focused primarily on evaluation methodology, we mapped use-cases and deployment paradigms, and audited translation-readiness reporting using a predefined governance framework (validation depth, privacy, fairness, regulatory alignment).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty-seven studies (2019-2025) were included. GANs predominated; other approaches included VAEs, diffusion models, LLM-based synthesis, and Bayesian networks. Dataset augmentation was the primary application, often improving downstream model performance for rare outcomes. Emerging applications included synthetic control cohorts and algorithmic bias mitigation. Translation-readiness reporting was limited: 34\/37 studies (92%) relied solely on internal validation, 9\/37 (24%) used formal privacy models, 6\/37 (16%) reported explicit fairness evaluations, and 6\/37 (16%) addressed regulatory alignment. Few studies distinguished \u201cno-release\u201d from \u201cdelayed-release\u201d paradigms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>A systemic gap exists between methodological innovation and deployment-readiness reporting. Model-based synthetic data show clear value for augmentation and class balancing, but inconsistent reporting of validation, privacy, fairness, and regulatory considerations limits confidence in clinical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We propose TRUST-SD (Transparency and Reporting for Utility, Safety, and Translation of Synthetic Data), an author-derived, preliminary, evidence-informed reporting checklist spanning 7 domains, as a starting point for community refinement and consensus-building.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag102","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T11:22:36Z","timestamp":1780312956000},"page":"2008-2028","source":"Crossref","is-referenced-by-count":1,"title":["Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-5880","authenticated-orcid":false,"given":"Simone","family":"Castagno","sequence":"first","affiliation":[{"name":"Department of Surgery, University of Cambridge , Cambridge, CB2 0QQ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7716-7801","authenticated-orcid":false,"given":"Alagu","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Cambridge , Cambridge, CB2 0QQ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4810-4197","authenticated-orcid":false,"given":"Ilias E","family":"Epanomeritakis","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Cambridge , Cambridge, CB2 0QQ,","place":["United 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