This chapter explores the deep impact of Generative AI (GenAI) on medical imaging, focusing on the application of generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion models. These models have enhanced diagnostic accuracy, image synthesis, and anomaly detection, facilitating cross-modality image generation, super-resolution, and denoising for improved image quality and workflow efficiency. GenAI addresses key challenges in medical imaging, including data scarcity and computational limitations, while improving image resolution in modalities like MRI and CT. Ethical considerations such as data privacy, algorithmic bias, and resource-intensive training are also explored. Through a historical lens, the chapter traces the evolution of medical imaging from X-rays to the integration of AI, emphasizing the continuous advancements made possible by GenAI technologies. The analysis of regulatory-approved platforms further underscores the tangible impact of GenAI in clinical environments, offering a comprehensive understanding of its present and future potential.