The low adherence rate to annual lung cancer screening (LCS) reduces potential mortality benefits. Interventions to improve adherence often do not consider individualized risk for nonadherence, which is vital in informing the design of tailored interventions. The authors systematically reviewed the literature and conducted a meta-analysis on predicting LCS nonadherence risk using machine learning.