  Artificial Intelligence (AI)-based models can help in diagnosing COVID-19
from lung CT scans and X-ray images; however, these models require large
amounts of data for training and validation. Many researchers studied
Generative Adversarial Networks (GANs) for producing synthetic lung CT scans
and X-Ray images to improve the performance of AI-based models. It is not well
explored how good GAN-based methods performed to generate reliable synthetic
data. This work analyzes 43 published studies that reported GANs for synthetic
data generation. Many of these studies suffered data bias, lack of
reproducibility, and lack of feedback from the radiologists or other domain
experts. A common issue in these studies is the unavailability of the source
code, hindering reproducibility. The included studies reported rescaling of the
input images to train the existing GANs architecture without providing clinical
insights on how the rescaling was motivated. Finally, even though GAN-based
methods have the potential for data augmentation and improving the training of
AI-based models, these methods fall short in terms of their use in clinical
practice. This paper highlights research hotspots in countering the data
scarcity problem, identifies various issues as well as potentials, and provides
recommendations to guide future research. These recommendations might be useful
to improve acceptability for the GAN-based approaches for data augmentation as
GANs for data augmentation are increasingly becoming popular in the AI and
medical imaging research community.
