〔Abstract〕 Objective To discuss the value of clinical radiomic nomogram(CRN) and deep convolutional neural network(DCNN) in distinguishing atypical pulmonary hamartoma(APH) from atypical lung adenocarcinoma(ALA). Methods A total of 307 patients were retrospectively recruited from two institutions. Patients in institution 1 were randomly divided into the training(n=184: APH=97, ALA=87) and internal validation sets(n=79: APH=41, ALA=38) in a ratio of 7:3, and patients in institution 2 were assigned as the external validation set(n=44: APH=23, ALA=21). A CRN model and a DCNN model were established, respectively, and the performances of two models were compared by delong test and receiver operating characteristic(ROC) curves. A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification. Results The areas under the curve(AUCs) of DCNN model were higher than those of CRN model in the training, internal and external validation sets(0.983vs0.968, 0.973vs0.953, and 0.942vs0.932, respectively), however, the differences were not statistically significant(p=0.23, 0.31 and 0.34, respectively). With a radiologist-AI competition experiment, AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS categories in ALA than radiologists. Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA, with the former performing better. AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.