We have seen numerous ground-breaking achievements in generative AI, particularly in the areas of computer vision, voice processing, and natural language processing, in recent times. Synthetic human faces, artworks, and cohesive essays on many subjects can be produced with excellent quality by generative adversarial networks and diffusion models. Because of their ability to learn complicated features from healthcare data and medical imaging, generative models are also revolutionizing health care domain. Thus, Generative AI are helping with healthcare and computer-aided diagnostics. The recent success of deep generative models in areas such as text-to-image conversion, diffusion modeling, and large language modeling has brought them immense attention. Learning interpretable representations and integrating different modalities or previous information from domain knowledge are common learning goals for early well-established approaches like variational autoencoders, generative adversarial networks, and normalizing flows. New developments in this area have the potential to open up enormous opportunities in the healthcare field.

This book provides a platform for research on deep generative models, with an emphasis on its healthcare applications. The book addresses the unanswered questions that stop these approaches from making a huge difference in real-world clinical practice. The goal of this book is to bring together a wide range of methodologies which are using generative models in health care-related contexts. The book leverages the recent methodological advancements in deep generative models to address critical health-care challenges across all data-types, paving the way for their practical integration into the healthcare system and elevate their impact on the future of healthcare.