Deep learning has gained significant attention in recent years for its ability to imitate human abilities, such as visual and auditory perception. These algorithms use statistics to find patterns in data and have shown promising results in various applications. Generative adversarial networks (GANs) have emerged as one of the most powerful generative models that can produce visually appealing samples. However, GANs suffer from several problems, such as mode collapse, non-convergence, and training instability. The generator's gradient is eliminated when the discriminator is optimal, resulting in slow learning and vanishing gradients. In this paper, we review the challenges associated with training GANs and the various methods proposed to address these issues. Recent research has proposed several approaches, including architectural modifications, regularization techniques, and alternative loss functions. Despite these efforts, the instability problem persists, and no studies to date have fully resolved the challenges associated with training GANs. Our survey presents a focused analysis of current GAN training advancements, with a special emphasis on addressing gradient vanishing in medical imaging. We highlight key challenges, review optimization techniques to mitigate these issues, and propose a framework for future research aimed at enhancing GAN stability and interpretability. This work contributes to advancing GANs in medical applications, improving their performance in generating realistic, high-quality medical images.