Background: Lung cancer screening (LCS) programs using low-dose computed tomography (LDCT) for early detection reduce mortality and have been widely recommended. LungFlag is a machine-learning risk prediction model that uses patient-level data to identify individuals at high risk of developing non-small cell lung cancer (NSCLC), prompting their physician to recommend screening with LDCT. Methods: A budget impact model was developed to estimate the costs associated with adoption of LungFlag as an adjunct to existing US Preventive Services Task Force (USPSTF) screening guidelines for a hypothetical US commercial health plan population of 1 million beneficiaries. The model calculates the total expected annual costs of screening for NSCLC with LDCT in scenarios with and without LungFlag, including healthcare resource utilization for detecting NSCLC and treatment of patients diagnosed with NSCLC. Incremental costs were evaluated over a 5-year period. Results: Among 36,803 USPSTF-eligible persons, we assumed that 4600 (12.5%) had already initiated LCS, leaving 32,203 persons who were candidates for pre-screening with LungFlag. The model estimated that 17 additional NSCLC diagnoses per year would be detected by screening when using LungFlag, with most in stage 1. Over 5 years, LungFlag was estimated to result in 33 fewer patients with stage 3 or stage 4 NSCLC at diagnosis and 22 fewer NSCLC-related deaths. Use of LungFlag increased annual costs during the first 2 years and provided cost savings from Year 4 onwards (Table). Cost savings from LungFlag were attributable to reductions in the costs of advanced NSCLC treatment. Conclusions: In a population of 1 million commercial health plan beneficiaries, the adoption of LungFlag as an adjunct to existing screening guidelines for USPSTF-eligible patients was estimated to prevent 22 additional NSCLC-related deaths, with a cost savings of $2.87 million over 5 years from a US commercial payer perspective.