How Generative AI Is Transforming Medical Imaging: A Practical Guide

Medical imaging is a crucial aspect of modern healthcare, as it enables the diagnosis and treatment of various diseases and conditions. However, developing and deploying AI models for medical imaging is challenging, due to the limited availability and quality of data, as well as the high complexity and diversity of imaging modalities and tasks. Generative AI models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and text-to-image diffusion models, offer a promising solution to these challenges, as they can generate realistic and diverse images from existing data or latent representations. In this chapter, we provide a practical guide on how generative AI is transforming medical imaging, by reviewing the state-of-the-art methods and frameworks, presenting some successful case studies in different domains and modalities, and discussing the future directions and opportunities for research and development.