Researchers tested 11 generative architectures across 9 datasets to evaluate the fidelity of synthetic healthcare records. The study establishes a standardized protocol for measuring statistical dependency and data utility. This framework helps PLOS contributors verify if privacy-preserving data maintains clinical accuracy. Practitioners can now better quantify the risk of using synthetic sets for training.