This text provides a comprehensive overview of generative artificial intelligence (AI) in radiology, detailing its transformative applications in medical imaging. It begins by explaining the fundamental differences between discriminative and generative AI, then introduces three core generative AI architectures: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, highlighting their individual mechanisms and strengths. The document then explores clinical applications across various modalities like MRI, CT, X-ray, and ultrasound, demonstrating how generative AI enhances image quality, accelerates scan times, and improves patient safety by reducing radiation exposure. Finally, it addresses crucial challenges and ethical considerations, such as the risk of "hallucinations," data privacy concerns, and algorithmic bias, while also examining the evolving ecosystem of research and commercialization and predicting the future role of radiologists as augmented "centaurs" in a human-AI partnership.

Research done with the help of artificial intelligence, and presented by two AI-generated hosts.

This episode will be fixed to remove the occurrences of "hash tag" introduced by the second stage AI.