Abstract: Objective: This study aimed to analyze the factors influencing artificial intelligence (AI) diagnostic system detection of pulmonary nodules on computed tomography (CT) scans, using a chest simulation phantom experiment with simulated lung nodules. Methods: A Toshiba (Canon) 64-slice spiral CT scanner was used. The phantom was scanned and images were reconstructed under varying scanning parameters (dose, reconstruction algorithm, and layer thickness) and non-scanning parameters (nodule size, density, and chest location). Subsequently, the AI diagnostic system performed pulmonary nodule detection on the images. Results: (1) No statistically significant difference was observed in the true positive rate of pulmonary nodules detected at different scanning doses. However, the low-dose mode produced a higher number of false positives. (2) Comparisons across the three iterative reconstruction algorithms revealed no statistically significant difference in the true positive rate of detected pulmonary nodules. However, the Strong group exhibited a higher true positive rate and a higher number of false positives. (3) The true positive rate of lung nodules detected in thin images was significantly higher than in thick images. (4) The true positive rate of pulmonary nodules located in the paramediastinum was significantly higher in the subpleural and lung parenchyma. (5) The true positive rate of nodules ≥8 mm was significantly higher than that of nodules ≤5 mm. (6) No statistically significant difference was observed in the true positive rate when detecting nodules with differing densities. Conclusion: Both CT scanning parameters (dose, reconstruction algorithm, and layer thickness) and non-scanning parameters (nodule size, density, and chest location) can influence the detection of pulmonary nodules by AI systems.