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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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