Objective：This study aims to evaluate the effectiveness of using an artificial intelligence system to assist radiologists in the diagnosis of fresh rib fractures with computed tomography（CT）. Methods：A dataset of 508 cases with fresh rib fracture CT images were collected and analyzed by 6 radiologists independently with the PACS system，the radiologists were divided into two groups：low seniority group（≤5 years）and high seniority group（>5 years）. After a washout period of four weeks，the CT images were evaluated again by the 6 radiologists with the assistance of AI system. The fracture type，site，and time of diagnosis were recorded for further analysis. The paired chi-square test was used to compare the sensitivity and specificity in diagnosis of fresh fracture with and without AI assistance. The receiver operating characteristic curve（ROC）and the area under curve（AUC）was calculated. Cohen’s kappa coefficient was used to analyze the consistency between low and high seniority radiologists in diagnosing rib fractures. Paired sample t test was used to compare the difference in diagnosis time. Results：A total of 2 883 fresh rib fractures were found in the 508 CT images. With the help of AI-assisted diagnosis，the diagnostic sensitivity of low and high seniority radiologists increased significantly from 77.95%，83.96% to 88.52%，90.98%（P < 0.001）；The average AUC increased from 0.902 to 0.948（P < 0.001）；The Cohen’s kappa coefficient between low and high seniority radiologists increased from 0.832 to 0.900. The average diagnosis time of each case decreased 28.43s，P < 0.001. Conclusion：With the AI assistance in the diagnosis of fresh rib fracture，the detection efficiency of rib fracture is improved and the diagnosis time is reduced.