After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulation about its distribution. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking approach to learn generative models efficiently that produce realistic Synthetic Data (SD). They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, simulations in the and marketing industrial sectors known as digital twins, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses a multitude of capabilities relevant to common problems in the healthcare: augmenting small dataset, correcting class imbalance, domain translation for rare diseases, let alone preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the COVID-19’s midst, the healthcare system is facing unprecedented challenges, many of which of are data related and could be alleviated by the capabilities of GANs. Considering these facts, publications concerning the development of GAN applied to OHD seemed to be severely lacking. To uncover the reasons for the slow adoption ofGANs for OHD, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD and eval-uating the SD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the choice of metrics ambiguous. We find many publications on the subject, starting slowly in 2017and since then being published at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation,consistency, benchmarking, data modeling, and reproducibility.
