![]() ![]() 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. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. 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. The digital twin concept could readily apply to modelling and quantifying disease progression. ![]() 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. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking approach to learn generative models efficiently that produce realistic Synthetic Data (SD). Vast potential is unexploited because of the fiercely private nature of patient-related data and regulation about its distribution. 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.
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