Mao et al. investigated how CT reconstruction parameters influence the ability of artificial intelligence (AI) to function as an independent reader in lung cancer screening, focusing specifically on the detection and classification of risk-dominant nodules. The study analyzed 300 low-dose CT scans from the NELCIN-B3 cohort in Tianjin, China, reconstructed under four different settings that varied in slice thickness and kernel type. Two radiologists performed consensus reads to serve as the reference standard, and the AI system’s performance was compared across reconstructions for both nodule detection and type classification. The results showed that reconstruction kernel choice, rather than slice thickness, had the strongest impact on AI performance. For scan-level identification of risk-dominant nodules, AI achieved the highest sensitivity with the medium-soft kernel (77.5%), while sensitivity dropped markedly with the sharp kernel (31.5%). Detection rates of the reference nodules remained relatively stable across all reconstructions (82.0%–87.4%). However, agreement with radiologists on nodule type classification varied substantially: 87.7% with the medium-soft kernel versus only 17.7% with the sharp kernel. Changing slice thickness from 1.0/0.7 mm to 2.0/1.0 mm did not significantly affect detection or classification accuracy. These findings underscore the need to optimize reconstruction settings to ensure AI-based tools can provide reliable results in real-world lung cancer screening programs.