This study evaluated the performance of a fully automated quantitative software, AVIEW ILA (Coreline), in detecting interstitial lung abnormalities (ILA) on routine chest CT compared with radiologists' visual analysis. The study included 336 participants who underwent chest CT for health screening. Inter-reader agreements were substantial for the presence of ILA (weighted κ, 0.74) and fair for its subtypes (weighted κ, 0.38). The quantification system showed 67.6% sensitivity, 93.3% specificity, and 90.5% accuracy in identifying ILA using a threshold of 5% in at least one zone. The best cut-off value for detecting ILA was 3.6%. The study concluded that the inter-reader agreement was substantial for ILA but only fair for its subtypes, and using an automated quantification system in routine clinical practice may aid in the objective identification of ILA.