A systematic review of Generative Adversarial Networks (GANs) in Plastic Surgery

Generative Adversarial Networks (GANs) are a form of deep learning architecture based on zero-sum game theory, which uses real data to generate realistic fake data. GANs use two opposing neural networks working, a generator and discriminator. They represent a powerful tool for generation of realistic synthetic patient data sets and have the potential to revolutionize research. This systematic literature review evaluates the scale and scope of GANs within Plastic Surgery, constructing a framework for its use and evaluation within subspecialties.
Source: Journal of Plastic, Reconstructive and Aesthetic Surgery - Category: Cosmetic Surgery Authors: Source Type: research