A Survey of Generative Adversarial Networks for Medical Images

Over the years, Generative Adversarial Networks ( $GANs$) have revolutionized the medical imaging industry for applications such as image synthesis, denoising, super resolution, data augmentation, and cross-modality translation. The objective of this review is to evaluate the advances, relevances, and limitations of $GANs$ in medical imaging. An organised literature review was conducted following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature considered included peer-reviewed papers published between $2020$ and $2025$ across databases including PubMed, IEEE Xplore, and Scopus. The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review. Thesis, white papers, communication letters, and non-English articles were not included for the same. CLAIM based quality assessment criteria were applied to the included studies to assess the quality. The study classifies diverse GAN architectures, summarizing their clinical applications, technical performances, and their implementation hardships. Key findings reveal the increasing applications of $GANs$ for enhancing diagnostic accuracy, reducing data scarcity through synthetic data generation, and supporting modality translation. However, concerns such as limited generalizability, lack of clinical validation, and regulatory constraints persist. This review provides a comprehensive study of the prevailing scenario of $GANs$ in medical imaging and highlights crucial research gaps and future directions. Though $GANs$ hold transformative capability for medical imaging, their integration into clinical use demands further validation, interpretability, and regulatory alignment.