This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays. By comparing the performance of a deep learning AI model with that of experienced radiologists, we seek to understand how AI can help improve fracture detection accuracy in clinical settings. The study analyzed 600 X-rays from both pediatric and adult patients, focusing on identifying fractures across different body parts, including the foot, ankle, knee, hand, wrist, and more. The findings show that integrating AI can increase radiologists' sensitivity in detecting fractures, potentially improving patient outcomes by reducing the number of missed injuries.

Inclusion Criteria:

• Patients aged 1 year or older.
• Musculoskeletal X-rays available in Digital Imaging and Communications in Medicine (DICOM) format.
• At least one digital plain radiograph of an appendicular body part, including the foot, ankle, knee, hand, wrist, elbow, shoulder, or pelvis.

Exclusion Criteria:

• Poor radiographic quality that precludes human interpretation.
• Radiographs of the lumbar, thoracic, and cervical spine, or facial/nasal bones.
• Radiographs that do not meet the inclusion criteria for appendicular body parts.

The study includes a retrospective cohort of pediatric and adult patients who underwent musculoskeletal radiographs between March 20 and May 8, 2023, in a single-center hospital setting.

Due to privacy concerns and the retrospective nature of the study, individual participant data (IPD) will not be shared. Data collected contains sensitive medical information that is protected under confidentiality agreements and GDPR regulations.