Comparative Performance of 3 Artificial Intelligence Systems for Lung Nodule Characterization in Low-Dose Computed Tomography Screening

PURPOSE: This study evaluates 3 artificial intelligence (AI) systems in detecting, characterizing, and classifying lung nodules on low-dose computed tomography (LDCT) scans of 100 subjects, assessing agreement with a reference standard and inter-vendor consistency. MATERIALS AND METHODS: Performance of 3 commercially available AI platforms-AI 1, AI 2, and AI 3-was retrospectively analyzed against evaluations by 2 thoracic radiologists, with discordances resolved by consensus as reference standard. Agreements were assessed for nodule presence, type (solid, part-solid, ground-glass), and Lung-RADS category using Cohen Kappa. Agreement for continuous measurements (nodule diameter and volume) across AI systems was evaluated using intraclass correlation coefficients (ICC). Group comparisons for continuous variables were performed using the Kruskal-Wallis test, with Mann-Whitney U tests for post hoc pairwise comparisons. Categorical variables were compared using χ 2 tests. Bland-Altman analysis evaluated variability in diameter and volume measurements. RESULTS: The 3 AI systems detected 435, 152, and 70 nodules, respectively, whereas radiologists identified 126 nodules ( P