RT Journal Article SR Electronic T1 Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies JF Thorax JO Thorax FD BMJ Publishing Group Ltd and British Thoracic Society SP thorax-2024-221662 DO 10.1136/thorax-2024-221662 A1 Geppert, Julia A1 Asgharzadeh, Asra A1 Brown, Anna A1 Stinton, Chris A1 Helm, Emma J A1 Jayakody, Surangi A1 Todkill, Daniel A1 Gallacher, Daniel A1 Ghiasvand, Hesam A1 Patel, Mubarak A1 Auguste, Peter A1 Tsertsvadze, Alexander A1 Chen, Yen-Fu A1 Grove, Amy A1 Shinkins, Bethany A1 Clarke, Aileen A1 Taylor-Phillips, Sian YR 2024 UL http://thorax.bmj.com/content/early/2024/09/25/thorax-2024-221662.abstract AB Objectives To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.Methods A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis.Results Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (−7% to −3% for correctly detecting/categorising people without actionable nodules; −8% to −6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150–750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance.Conclusions AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design.PROSPERO registration number CRD42021298449.All data relevant to the study are included in the article or uploaded as supplementary information.