Generative adversarial networks (GANs) have gained significant attention in medical imaging due to their ability to generate data without explicitly modeling probability distributions. This paper reviews recent advances in medical imaging using GANs, highlighting their applications in image reconstruction, segmentation, detection, classification, and cross-modality synthesis. GANs consist of two networks: a generator that creates images and a discriminator that distinguishes real from generated images. The adversarial training scheme has proven effective in domain adaptation, data augmentation, and image-to-image translation. However, challenges such as mode collapse and training instability remain. Variants of GANs, including those with different loss functions and architectures, have been developed to address these issues. Medical imaging applications of GANs include image reconstruction, synthesis, segmentation, and detection. For example, GANs have been used to denoise low-dose CT images, synthesize MR images from CT data, and segment organs in X-ray images. The paper also discusses the potential of GANs in overcoming data scarcity and privacy concerns in medical imaging. Despite their promise, challenges such as domain shift, evaluation metrics, and computational efficiency remain. The review concludes that GANs have the potential to significantly impact medical imaging, but further research is needed to address existing challenges and improve their practical applications.Generative adversarial networks (GANs) have gained significant attention in medical imaging due to their ability to generate data without explicitly modeling probability distributions. This paper reviews recent advances in medical imaging using GANs, highlighting their applications in image reconstruction, segmentation, detection, classification, and cross-modality synthesis. GANs consist of two networks: a generator that creates images and a discriminator that distinguishes real from generated images. The adversarial training scheme has proven effective in domain adaptation, data augmentation, and image-to-image translation. However, challenges such as mode collapse and training instability remain. Variants of GANs, including those with different loss functions and architectures, have been developed to address these issues. Medical imaging applications of GANs include image reconstruction, synthesis, segmentation, and detection. For example, GANs have been used to denoise low-dose CT images, synthesize MR images from CT data, and segment organs in X-ray images. The paper also discusses the potential of GANs in overcoming data scarcity and privacy concerns in medical imaging. Despite their promise, challenges such as domain shift, evaluation metrics, and computational efficiency remain. The review concludes that GANs have the potential to significantly impact medical imaging, but further research is needed to address existing challenges and improve their practical applications.