AI-powered Hyperrealism: Next Step in Cinematic Rendering?

Document Type

Journal Article

Publication Date

1-1-2024

Journal

Radiology

Volume

310

Issue

1

DOI

10.1148/radiol.231971

Abstract

Background Recent advancements in artificial intelligence (AI)-powered image generation present opportunities to enhance three-dimensional medical images. Diffusion, an iterative denoising process, represents the standard of many of the current tools used for this purpose. Purpose To demonstrate the current capabilities of diffusion technology by using Midjourney, version 5.2, a text-to-image generative AI tool, and present a practical guide for its use. Materials and Methods This exploratory study investigates the principles, parameters, and prompt engineering techniques for generating images focusing on Midjourney from July 27 to August 3, 2023. Step-by-step instructions show the innate capability of this technology in creating realistic medical images. Results Thirty images were selected, including eye, skin, and vascular aneurysm images. Varying prompt phrasing and weighting techniques allowed for the customization of output image characteristics. Although the details of Midjourney's model training are confidential, it is estimated that it was trained on at least hundreds of millions of images from the web. Anatomic fidelity was not always maintained because the training data set is not necessarily based on accurate medical images. There are shortcomings in this nascent technology regarding its ability to create entities such as digits of the hand or precise text. Conclusion AI image generation has the potential to improve three-dimensional medical images for certain applications through added visual detail and appeal but ongoing collaboration is needed between radiologists and AI developers due to the overreliance on art and photography in the training data, which may result in inaccurate anatomic results. Moreover, the evolving landscape of ethical discussions and copyright stipulations warrants close attention. © RSNA, 2024

Department

Radiology

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