Characteristics of patients and RFs.

To evaluate the diagnostic accuracy of artificial intelligence (AI) assisted radiologists and standard double-reading in real-world clinical settings for rib fractures (RFs) detection on CT images. This study included 243 consecutive chest trauma patients (mean age, 58.1 years; female, 166) with rib CT scans. All CT scans were interpreted by two radiologists. The CT images were re-evaluated by primary readers with AI assistance in a blinded manner. Reference standards were established by two musculoskeletal radiologists. The re-evaluation results were then compared with those from the initial double-reading. The primary analysis focused on demonstrate superiority of AI-assisted sensitivity and the noninferiority of specificity at patient level, compared to standard double-reading. Secondary endpoints were at the rib and lesion levels. Stand-alone AI performance was also assessed. The influence of patient characteristics, report time, and RF features on the performance of AI and radiologists was investigated. At patient level, AI-assisted radiologists significantly improved sensitivity by 25.0% (95% CI: 10.5, 39.5; P