Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels - Peters, A. A.; Wiescholek, N.; Muller, M.; Klaus, J.; Strodka, F.; Macek, A.; Primetis, E.; Drakopulos, D.; Huber, A. T.; Obmann, V. C.; Ruder, T. D.; Roos, J. E.; et al. (Sci Rep, 2024)
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.
Keywords: journalArticle; *Artificial Intelligence; *Lung Neoplasms/diagnostic imaging; *Radiologists; *Solitary Pulmonary Nodule/diagnostic imaging; *Tomography, X-Ray Computed/methods; Adult; Aged; Ct; Female; Humans; Lung cancer; Male; Middle Aged; Multiple Pulmonary Nodules/diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted/methods; Radiologists; Software