{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T15:10:14Z","timestamp":1790953814149,"version":"4.1.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"Abstract<\/jats:title>\n \n Objectives<\/jats:title>\n To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage of tasks defined in nodule management recommendations, and assess their peer-reviewed evidence.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods<\/jats:title>\n \n Six core tasks in LCS (nodule detection, classification, measurement, growth assessment, malignancy risk estimation, and structured management) were derived from 4 nodule management recommendations: Lung-RADS 2022, British Thoracic Society (BTS) guidelines, European Union Position Statement (EUPS), and European Society of Thoracic Imaging (ESTI). Products were identified through\n www.healthairegister.com<\/jats:ext-link>\n . Vendors confirmed capabilities using a standardized questionnaire; public documentation supplemented non-responders. Task coverage was calculated as the percentage of functional overlap (0\u2013100%) per recommendation. Peer-reviewed evidence was evaluated using a six-level efficacy framework and assessed for study characteristics.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In total, 16 products from 16 vendors were included; 10 vendors completed questionnaires. Analysis showed that 14 products detect and measure solid and subsolid nodules, 12 support growth assessment, and 9 provide malignancy risk estimation (PanCan in 5, AI-based scores in 4). No product provides support for endobronchial or cystic lesions. High task coverage (>\u200975%) was observed in 10 products for EUPS and 4 for BTS, whereas no product achieved high coverage for Lung-RADS or ESTI. Overall, 60 peer-reviewed studies were identified; 7% were prospective and evidence clustered at lower efficacy levels: 70% assessed diagnostic accuracy, while none reported patient outcomes or societal impact.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Numerous CE-certified AI products could support CT-based lung cancer screening, but gaps in task coverage and predominantly lower-level evidence necessitate cautious, monitored implementation.<\/jats:p>\n <\/jats:sec>\n \n Key Points<\/jats:title>\n \n \n Question<\/jats:italic>\n <\/jats:bold>\n Do commercially available AI products for lung nodule analysis functionally cover international nodule management recommendation-defined tasks, and what peer-reviewed clinical evidence supports them?<\/jats:italic>\n <\/jats:p>\n \n \n Findings<\/jats:italic>\n <\/jats:bold>\n AI products support standard nodule detection and measurement in line with management recommendations but lack support for endobronchial or cystic lesions and high-level clinical evidence<\/jats:italic>\n .\n <\/jats:p>\n \n \n Clinical relevance<\/jats:italic>\n <\/jats:bold>\n CE-marked AI products can assist radiologists with core lung cancer screening tasks, but capability gaps exist. Limited high-level clinical evidence complicates integrating AI into guidelines, securing reimbursement, and formulating recommendations for its use in lung cancer screening programs<\/jats:italic>\n .\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1007\/s00330-026-12580-x","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:49:02Z","timestamp":1778172542000},"page":"7199-7209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8702-3640","authenticated-orcid":false,"given":"Noa","family":"Antonissen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Schalekamp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Horst K.","family":"Hahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kicky G.","family":"van Leeuwen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin","family":"Jacobs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"12580_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray F, Laversanne M, Sung H et al (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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C.J. declares no funding for this study. C.J. receives research grants and royalties to the host institution from MeVis Medical Solutions, a public-private research grant with Philips Medical Systems, and a public-private research grant with Siemens Healthineers. C.J. received speaker fees from Canon Medical Systems and Johnson & Johnson, all outside of the submitted study. S.S. receives a research grant from Annalise.ai. The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The analysis was descriptive and did not require complex statistics.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Not applicable. No human or animal subjects were involved in this study.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was not required because this study deals with public data and data provided by vendors.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Our dataset partly overlaps with two prior broad reviews of CE-certified radiology AI products: van Leeuwen et al [\n \n ] and Antonissen et al [\n \n ]. We extend this work by focusing specifically on lung nodule analysis products, updating the product list and literature through January\u00a01, 2025, systematically mapping each product\u2019s lung nodule analysis capabilities, and assessing how these capabilities cover the tasks defined in major international nodule management recommendations. The overlap is detailed in the \u201cMaterials and methods\u201d section.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}