  This review presents a comprehensive study on the role of GANs in addressing
the challenges related to COVID-19 data scarcity and diagnosis. It is the first
review that summarizes the different GANs methods and the lungs images datasets
for COVID-19. It attempts to answer the questions related to applications of
GANs, popular GAN architectures, frequently used image modalities, and the
availability of source code. This review included 57 full-text studies that
reported the use of GANs for different applications in COVID-19 lungs images
data. Most of the studies (n=42) used GANs for data augmentation to enhance the
performance of AI techniques for COVID-19 diagnosis. Other popular applications
of GANs were segmentation of lungs and super-resolution of the lungs images.
The cycleGAN and the conditional GAN were the most commonly used architectures
used in nine studies each. 29 studies used chest X-Ray images while 21 studies
used CT images for the training of GANs. For majority of the studies (n=47),
the experiments were done and results were reported using publicly available
data. A secondary evaluation of the results by radiologists/clinicians was
reported by only two studies. Conclusion: Studies have shown that GANs have
great potential to address the data scarcity challenge for lungs images of
COVID-19. Data synthesized with GANs have been helpful to improve the training
of the Convolutional Neural Network (CNN) models trained for the diagnosis of
COVID-19. Besides, GANs have also contributed to enhancing the CNNs performance
through the super-resolution of the images and segmentation. This review also
identified key limitations of the potential transformation of GANs based
methods in clinical applications.
