In a study examining whether clinicians could distinguish synthetic radiology scans from genuine ones, radiologists correctly identified AI-generated images only about three quarters of the time. The findings suggested that cross-sectional imaging, particularly CT and MRI, were more difficult to classify accurately than x-rays or ultrasound. The study included 182 radiologists, who completed an online survey featuring 30 images comprising 20 AI-generated and 10 real radiological images. Researchers created the synthetic images using the Dreambooth fine-tuning approach applied to Stable Diffusion v2.1. Radiologists correctly classified a median of 77.8% of images. Detection rates were similar for AI-generated images (75.0%) and genuine images (83.4%), with no statistically significant difference. Performance varied by imaging modality, with correct classification rates of 88% for ultrasound, 91% for x-ray, 70% for CT, and 77% for MRI. Years of radiology experience and self-reported familiarity with AI did not affect performance. However, radiologists with specialist expertise relevant to the image being assessed achieved higher classification accuracy (80.7%) than those without a matching subspecialty (76.9%) interest (p=0.012). The findings suggested AI-generated radiological images could support applications such as AI model training, while the difficulty distinguishing synthetic from genuine CT and MRI images highlighted potential for misuse.