Fracture detection using radiography is crucial for effective patient management. Despite advances, missed fractures remain a significant issue. This study evaluates the diagnostic performance of a deep learning model versus radiologists in identifying fractures on musculoskeletal X-rays. For the purpose of our study, we collected a study sample (n_SAMPLE) of 600 pediatric and adult radiographs, and retrospectively analyzed the images by two ground truth readers, four radiologists in a multi-reader study with varying experience, and an AI model (Carebot AI Bones 1.2.2 Carebot s.r.o.). The ground truth was reached for 548 images (n_GT), including 95 fracture cases (n_FRACTURE) and 453 normal cases (n_NORMAL). The results demonstrated that the AI system achieved a sensitivity (Se) of 0.884 (0.804–0.934) and a specificity (Sp) of 0.879 (0.845–0.906). In comparison, the radiologists' sensitivity ranged from 0.695 (0.596–0.778) to 0.832 (0.744–0.894) and their specificity ranged from 0.962 (0.941–0.976) to 0.993 (0.981–0.998). The AI model outperformed radiologists in Se across various body parts, particularly in areas with higher fracture prevalence, while showing comparable Sp in some categories. This study highlights the potential of AI to enhance diagnostic accuracy in clinical practice.
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