ObjectiveTo evaluate the accuracy, efficiency, and clinical application potential of artificial intelligence (AI) technology in the imaging assessment of osteoporotic vertebral fractures (OVF), providing objective evidence for optimizing early fracture diagnosis and grading. MethodsA retrospective analysis was conducted on chest CT imaging data from 100 OVF patients and 300 normal controls. An AI-assisted diagnostic system and radiologists independently evaluated the thoracic 1(T1) to lumbar 1(L1) vertebrae in a double-blind manner. The consensus of three senior radiologists served as the gold standard. Agreement analysis and efficiency comparison were performed between the two methods. ResultsThe AI system demonstrated high agreement with radiologists in vertebral fracture detection (κ=0.83, 95% CI: 0.76-0.90). The AI system achieved a sensitivity of 94.2%, significantly higher than that of the radiologist group (86.7%, P<0.05). The AI system completed single-case whole-spine CT analysis in (16.6± 3.2) seconds, significantly faster than the radiologist group (89.7±21.4) seconds (P<0.001). When AI pre-screening was combined with targeted radiologist review, the average interpretation time was reduced by 81.5%. ConclusionThe conventional Genant semi-quantitative visual assessment method suffers from poor reproducibility. Integrating AI software (e.g., uAI Spine Analyzer) into vertebral fracture evaluation can enhance radiologists’ efficiency and significantly improve diagnostic accuracy, particularly in detecting early mild fractures. This AI-assisted approach serves as an effective clinical diagnostic tool, facilitating close monitoring of bone health and contributing to the strategic goal of “healthy aging.”