Objective To explore the application of artificial intelligence assisted low-dose chest CT in the differential diagnosis of benign and malignant pulmonary nodules. Methods 83 patients who underwent low-dose chest CT examination at Xinxiang Tongmeng Hospital from March 2020 to July 2021 were selected. They were divided into two groups based on different methods of examination: simple manual examination group and artificial intelligence assisted examination group. The diagnostic results of lung nodules in both groups were observed. Results The positive rate of artificial intelligence assisted film reading in diagnosing pulmonary nodules was 86.75%, which was higher than that of manual film reading of 68.67%, and the difference was statistically significant (x 2 =6.549, P =0.013); The Kappa test showed weak consistency between the two reading methods (Kappa value=0.196), with P >0.05; The detection rate of pulmonary nodules with a diameter of 3~7mm using artificial intelligence assisted film reading was 93.44%, significantly higher than that of 85.25% using manual film reading, and the difference was statistically significant (P<0.05). However, there was no statistically significant difference in the detection rate of pulmonary nodules with a diameter of 0~3mm and 7~20mm between the two film reading methods (P >0.05); Using pathological results as the gold standard, the ROC curve was plotted, and the results showed that the AUC for diagnosing malignant pulmonary nodules using manual film reading and artificial intelligence assisted film reading were 0.742 (95%CI: 0.514, 0.921) and 0.830 (95%CI: 0.701, 1.00) respectively. The specificity, sensitivity, positive predictive value, and negative predictive value of the two film reading methods were not statistically significant (P >0.05). Conclusion Artificial intelligence assisted low-dose chest CT can improve the accuracy of diagnosing benign and malignant pulmonary nodules, and increase the detection rate of pulmonary nodules with a diameter of 3~7mm. However, it is consistent with the specificity, sensitivity, positive predictive value, and negative predictive value of manual film reading for the diagnosis of pulmonary nodules.