Deep learning-based automated segmentation and volumetric nodule analysis improved inter-observer consistency in pulmonary nodule assessment during lung cancer screening, reducing management discrepancies versus manual measurement. Using a deep learning pipeline built on nnU-Net v2, investigators compared manual diameter readings with semiautomated measurements and full 3D volumetry. Across solid nodules categorized under Lung-RADS risk categories 2, 3, 4A and 4B, intraclass correlation coefficients exceeded 0.90 for all measurement techniques, while Bland-Altman analysis showed narrower limits of agreement and lower systematic bias for semiautomated readings. The practical win for screening programs is tighter agreement among radiologists, which can reduce avoidable category “flips” that drive downstream diagnostic pathways and patient anxiety.