Hsu HH, Ko KH, Chou YC, et al. - The present study was conducted to correlate the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes. Researchers retrospectively collected a total of 150 multidetector computed tomography (CT) datasets containing 340 nodules ≤10 mm in diameter. They used a CAD with a vessel-suppressed function to interpret the images. Analysis of variance was used to compare diagnostic performances and reading times. The results revealed that the second-reading mode and concurrent-reading mode may improve detection performance for all readers in both screening and clinical routine practice in CAD for lung nodules at CT. As per the findings, concurrent use of CAD is more efficient for both junior and senior readers.