Abstract: Objective: To investigate the applicability of artificial intelligence-assisted diagnosis system in detecting pulmonary nodules and distinguishing benign and malignant nodules. Methods: Patients who underwent chest computed tomography (CT) from March 2022 to March 2023 at our hospital and who were followed-up with CT-guided needle biopsy or surgical procedures to determine the pathological nature were included in the study. The criteria for identifying true positive nodules were identified on the basis of the image analysis based on AI and two radiologists who identified suspicious lesions and referred to multiplanar reconstruction and three-dimensional reconstruction and other images to determine the existence of lung nodules; the findings from the two reports were consistent. The detection value of benign and malignant nodules obtained from AI and radiologists for true-positive nodules were compared. Results: AI detected 1337 nodules in 113 patients; radiologists detected 774 nodules, and verified the coexistence of 1079 true-positive nodules. The detection rate of true positive nodules (98.98%) by AI software was higher than that of radiologists (71.27%), the missed diagnosis rate (1.02%) was lower than that of radiologists (28.27%), and the misdiagnosis rate (23.91%) was higher than that of radiologists (0.46%). The true positive detection rate of AI for nodules with diameters <5 and 5~10 mm (98.69%; 100.00%) was higher than that of radiologists (60.59%; 80.25%); the detection rate of true positive nodules with diameters >10 mm (98.08%) was slightly higher than that of radiologists (94.87%), but the difference was not statistically significant. Further large-sample clinical studies are needed to verify our findings. The detection rates of mixed ground-glass and calcified nodules (98.47%; 98.79%; 100.00%; 100.00%) from AI imaging were higher than those of the radiologists (75.52%; 68.02%; 72.73%; 84.66%). In the identification of benign and malignant nodules, 115 nodules in 113 patients were confirmed by pathological examination, of which 98 nodules were obtained by interventional surgery or CT-guided biopsy in our hospital, and 17 nodules were determined by follow-up diagnosis and during treatment at other hospitals. The sensitivity (97.47%), specificity (80.56%), and accuracy (92.17%) of the radiologists for the identification of benign and malignant nodules were higher than those of AI (93.67%, 66.67%, and 85.22%, respectively); however, the difference in sensitivity and specificity was not significant and might have been caused by sample selection bias. AI achieved high agreement with pathological results (Kappa value, 0.637), and radiologists achieved almost complete agreement (Kappa value, 0.811). Conclusion: AI-assisted diagnosis has a high detection rate and sensitivity for pulmonary nodules, which can greatly reduce the missed diagnosis rate, but increases the misdiagnosis rate. It can serve as an auxiliary aid for clinical diagnosis in identifying benign and malignant pulmonary nodules, but it cannot replace radiologists.