Generative adversarial networks describe deep-learning algorithms that are able to synthesize highly realistic images, videos and audio. The most commonly known application of GANs are deepfakes, which has given them somewhat of a bad reputation. However, they can be used as a source of good as well. Due to their ability pattern match images they can have important benefits for medical diagnosis for example.
Since researches in the medical often lack sufficient training data due to privacy concerns it is often difficult to train deep-learning algorithms. GANs can be used instead to synthesize images that are virtually equivalent to the real ones in the quantity needed.