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Artificial intelligence-based Text-to-image Generation of Cardiac CT

Authors :
Michelle C. Williams
Steven E. Williams
David E. Newby
Source :
Williams, M C, Williams, S & Newby, D E 2023, ' Artificial intelligence-based Text-to-image Generation of Cardiac CT ', Radiology: Cardiothoracic Imaging, vol. 5, no. 2, e220297 . https://doi.org/10.1148/ryct.220297, Radiol Cardiothorac Imaging
Publication Year :
2023

Abstract

Artificial intelligence (AI) has revolutionized art and design industries due to its ability to create images from natural language text. Such models also contain latent medical information. Text-to-image AI thus has the potential to create synthetic data sets for research, education, and communication. However, these may be indistinguishable from real images, causing issues with trust and potential misrepresentation. Radiologists and clinicians must be aware of the feasibility of creating “deep fake” medical images (Figure). Inconsistencies in anatomy or image texture could identify AI images, but there are currently no technical solutions for identification.

Details

Language :
English
Database :
OpenAIRE
Journal :
Williams, M C, Williams, S & Newby, D E 2023, ' Artificial intelligence-based Text-to-image Generation of Cardiac CT ', Radiology: Cardiothoracic Imaging, vol. 5, no. 2, e220297 . https://doi.org/10.1148/ryct.220297, Radiol Cardiothorac Imaging
Accession number :
edsair.doi.dedup.....33190751ff7a5100e59cb5f773244fcd
Full Text :
https://doi.org/10.1148/ryct.220297