Objective:To explore the predictive value of artificial intelligence (AI) software in identifying benign and malignant lung nodules and predicting the pathological types of lung nodules.MethodsPatients with lung nodules confirmed by pathological examination were collected, who underwent high-risk lung nodule screening at the Minhang Branch of Affiliated to Fudan University Cancer Hospital from September 2020 to August 2024. AI software was used to analyze the benignity and malignancy and pathological types of pulmonary nodules, and consistency with pathological results was tested. The diagnostic performance of the AI software was evaluated through the area under the receiver operating characteristic (ROC) curve.ResultsA total of 62 patients with pulmonary nodules were included in the study, including 4 cases of inflammatory nodules, 4 cases of carcinoma in situ, 2 cases of atypical adenomatous hyperplasia, 16 cases of microinvasive adenocarcinoma, 32 cases of invasive adenocarcinoma, and 4 cases of squamous cell carcinoma. The sensitivity, specificity, and accuracy of the pulmonary nodule software in diagnosing the benignity and malignancy of pulmonary nodules were 98.28%, 75.00%, and 96.80%, respectively. The area under the ROC curve for diagnosing benign and malignant pulmonary nodules by AI analysis was 0.866. The consistency between the AI software's predictions of pulmonary nodule pathological types and the pathological results was tested, with a Kappa value of 0.859.ConclusionAI software based on computed tomography (CT) target scanning can effectively distinguish between benign and malignant lung nodules during lung cancer screening. It provides a reference for predicting the pathological types of lung nodules and is valuable for optimizing clinical surgical procedures and enabling precise management of patients with pulmonary nodules.