Generative AI is at the forefront of a paradigm shift in medical imaging, delivering transformative advancements that enhance diagnostic precision, improve image quality, and streamline treatment planning and disease monitoring processes. Cutting-edge techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are playing a pivotal role in overcoming key challenges in medical imaging, including noise reduction, image reconstruction, and cross-modality image translation. GANs excel in generating high-resolution synthetic images that replicate real-world datasets, supporting data augmentation and improving diagnostic model training, while VAEs offer robust capabilities in creating lower-dimensional representations of complex imaging data, aiding in noise reduction and segmentation tasks.

This paper delves deeply into the applications of generative AI, providing detailed insights into how these technologies are reshaping the healthcare landscape. It examines the application spectrum, from enhancing imaging modalities and improving segmentation accuracy to fostering innovations in personalised medicine. Additionally, it explores technical challenges, such as the computational demands of training generative models and the complexities of achieving interpretability, alongside critical ethical considerations, including data privacy, algorithmic fairness, and clinical accountability.

Through a synthesis of case studies, comparative analyses with traditional methodologies, and an exploration of future directions, the paper underscores the transformative potential of generative AI to redefine the capabilities of medical imaging. The discussion advocates for responsible development and deployment, emphasising the importance of transparent, ethical, and collaborative efforts to integrate these technologies into clinical practice, ultimately optimising patient care and healthcare outcomes.