  Despite the growing availability of high-quality public datasets, the lack of
training samples is still one of the main challenges of deep-learning for skin
lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing
alternative to alleviate the issue, by synthesizing samples indistinguishable
from real images, with a plethora of works employing them for medical
applications. Nevertheless, carefully designed experiments for skin-lesion
diagnosis with GAN-based data augmentation show favorable results only on
out-of-distribution test sets. For GAN-based data anonymization $-$ where the
synthetic images replace the real ones $-$ favorable results also only appear
for out-of-distribution test sets. Because of the costs and risks associated
with GAN usage, those results suggest caution in their adoption for medical
applications.
