Authors - Sarika Pabalkar(Wagh), Kirti Jain Abstract - Generative adversarial networks, or GANs, are strong tools for improving medical pictures because they can make high-quality virtual images that can be used to solve problems like limited datasets, image unpredictability, and poor diagnosis accuracy. This research looks at all the ways that GAN can be used in medical imaging and highlights the most important improvements in the process of segmentation reconstruction, disease identification, and cross-modal synthesis. A structured methodology based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was employed to identify, screen, and analyze 42 peer-reviewed articles published between 2020 and 2025 across databases such as PubMed, Scopus, IEEE, and ScienceDirect. The review compares widely used GAN architectures—including CycleGAN, Pix2Pix, DCGAN, and ProGAN— evaluating their strengths, limitations, and suitability for different imaging modalities such as MRI, CT, X-ray, ultrasound, and mammography. There are significant problems, including mode collapse, instability in training, no consistent evaluation standard, and few opportunities for clinical validation. The results show the increasing importance of GANs in generating clinically useful data for rare disease information and for cross-modality tasks. The discussion also indicated future interventions that would focus on model stability, ethical use, clinical incorporation, and generalization across populations. The aim of the review is to help fellow researchers and practitioners through assessing state-of-the-art GAN approaches and defining gaps that will need to be addressed prior to more adaptation of GANs in medical imaging.
