Researchers at Stanford developed an Artificial Intelligence (AI) Model called 'RoentGen,' based on Stable Diffusion and fine-tuned on a Large Chest X-ray and Radiology Dataset - MarkTechPost

Latent diffusion models (LDMs), a subclass of denoising diffusion models, have recently acquired prominence because they make generating images with high fidelity, diversity, and resolution possible. These models enable fine-grained control of the image production process at inference time (e.g., by utilizing text prompts) when combined with a conditioning mechanism. Large, multi-modal datasets like LAION5B, which contain billions of real image-text pairs, are frequently used to train such models. Given the proper pre-training, LDMs can be used for many downstream activities and are sometimes referred to as foundation models (FM). LDMs can be deployed to end users more easily because their denoising process operates in a relatively low-dimensional latent space and requires only modest hardware resources.