Fracture detection on musculoskeletal radiographs remains challenging, particularly when findings are subtle or visible on only one projection. This retrospective two-centre multi-reader diagnostic performance study evaluated an artificial intelligence system at the level of the complete radiographic examination and compared its performance with seven anonymized readers. Radiographic examinations from AGEL Šternberk Hospital and AGEL Vítkovice Hospital, acquired between 15 December 2025 and 25 January 2026, were retrospectively analysed. The primary endpoint was study-level fracture positivity, defined as at least one positive image within an examination; the same aggregation rule was applied to AI and reader assessments. Examinations of the spine, ribs, and hemithorax were excluded because these anatomies were outside the supported scope.