Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit
Abstract
Objectives: To evaluate the clinical applications and translation readiness of model-based synthetic tabular data in healthcare, and identify gaps in governance reporting that may hinder translation.
Materials and methods: We systematically searched Ovid MEDLINE and Embase (2010-August 2025; PROSPERO: CRD42025635514) for studies that generated and applied model-based synthetic tabular data in clinical contexts. Screening used a "human-in-the-loop" 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).
Results: 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 "no-release" from…
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