Use of a deep-learning convolutional neural network (CNN) -- a form of artificial intelligence -- can help reveal patterns on chest computed tomography (CT) scans that identify smokers at high long-term risk for lung cancer well beyond the Centers for Medicare & Medicaid Services (CMS) criteria for lung screening eligibility, according to the results of an analysis published in the Annals of Internal Medicine. Investigators sought to create and validate a CNN -- that is, the CXR-LC model -- with the ability to predict long-term incident lung cancer via the use of data typically available in a patient's electronic medical record, including chest radiographs, sex, age, and current smoking status. The CXR-LC model was developed in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, which included to total of 41,856 patients. The final CXR-LC model was validated in additional smokers from the PLCO study (n 5615; 12-year follow-up) and National Lung Screening Trial (NLST) heavy smokers (n 5493; 6-year follow-up). There were more current smokers (50.4% vs 20.2%, respectively) and higher mean pack-years (55.7 vs 35.4,