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Real or not real? Can radiologists distinguish artificial intelligence generated radiological images from real ones?
1British Heart Foundation Centre for Research Excellence, Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4TJ, United Kingdom.
Aim:
Artificial intelligence (AI) models can create radiological images. We aimed to determine whether radiologists could distinguish AI-generated from real images, and factors associated with correct classification.
Materials And Methods:
AI-generated images were made using an implementation of the Dreambooth fine-tuning approach applied to Stable Diffusion v2.1. Radiologists were asked to classify images as real (n = 10) or AI-generated (n = 20) and their confidence in this decision (1 least, 5 most) in an online form.
Results:
182 radiologists completed the survey. The median proportion of correctly identified images per respondent was 77.8% (interquartile range, IQR 70.0, 86.7%), with no difference between AI-generated (75.0%, IQR 70.5, 87.1%) and real images (83.4%, IQR 74.2, 92.6%, p = 0.19). Ultrasound and X-ray were more likely to be correctly identified than cross-sectional images like CT or MRI (88%, 91%, 70% and 77% respectively, p = 0.015). Mean confidence was similar for AI-generated and real images (3.50 ± 0.23 versus 3.56 ± 0.23, p = 0.49). There was no difference in classification based on number of years of experience (p = 0.57) or familiarity with AI (p = 0.37). However, radiologists with relevant specialist interests were more likely to correctly classify images (80.7 ± 1.3% versus 76.9 ± 0.8%, p = 0.012).
Conclusions:
Radiologists were only able to correctly identify three-quarters of AI-generated images. This was impacted by sub-specialist expertise but not the number of years of experience or familiarity with AI.
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