{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:05:43Z","timestamp":1784077543090,"version":"3.55.0"},"reference-count":114,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"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":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This systematic review evaluates the available evidence on the efficacy of commercially available AI-software applications for lung nodule assessment on CT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>In adherence to PRISMA guidelines, a thorough search of electronic databases was conducted (January 2012\u2013November 2024) to identify studies on CE-marked and\/or FDA-cleared AI-based systems for evaluating pulmonary nodules on CT scans. Articles were systematically categorised using the Radiology AI Deployment and Assessment Rubric (RADAR) to assess the evolution of efficacy over time across various hierarchical levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 95 studies were included. Between 2012 and 2024, the number of studies increased from 14 in 2012\u20132017 to a total of 95 by 2024. The studies were categorised using the RADAR efficacy model. In the early period (2012\u20132017), most studies focused on lower efficacy levels, with Level-1 (technical efficacy) and Level-2 (diagnostic accuracy) dominating at 83.3%. During this period, AI applications were mainly focused on quantification (46.7%), while malignancy prediction was addressed in only 6.7% of studies. By 2024, Levels-3 (diagnostic thinking efficacy), 4 (therapeutic efficacy), and 5 (patient outcomes) accounted for over a third of all studies. Malignancy prediction further increased to 28.6%, and nodule characterisation emerged in 4.5% of studies. Despite advancements, research on patient outcomes and cost-effectiveness efficacy (Levels-5 and 6) remains limited. Also, all included studies demonstrated high risk of bias in at least one domain, and nearly two-thirds involved vendor funding or co-authorship.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In conclusion, the growing interest and investment in AI technologies for thoracic radiology have driven significant advancements in lung nodule assessment on CT-scans. Gaps remain in assessing patient outcomes and societal implications, which must be addressed to fully realise AI\u2019s potential in clinical practice and public health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>What is the current level of scientific evidence supporting commercially available AI tools for pulmonary nodule assessment on CT, and is this sufficient to support their clinical implementation<\/jats:italic>\n                      ?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Research on AI for pulmonary nodule assessment has surged, with the focus evolving from basic technical performance in the early years to higher clinical outcomes by 2024<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>This study provides critical insights into the evolving role of AI in lung nodule assessment on CT-scans, highlighting advancements in technology and identifying key gaps in current research. Addressing these is essential for optimising AI\u2019s clinical application and improving patient outcomes<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12745-8","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T23:17:14Z","timestamp":1784071034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review"],"prefix":"10.1007","author":[{"given":"Jasika","family":"Paramasamy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asabi","family":"Leliveld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan-Willem","family":"Groen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Willems","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim G. 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Visser.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Authors J.P., A.L., J.-W.G., B.W., T.A.M., and A.O. declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. A.v.d.L. reports grants or consulting fees from the Dutch Heart Foundation, Brain Foundation Netherlands, The Netherlands Organisation for Health Research and Development, and unrestricted grants from Cerenovus, Medtronic USA Inc., Penumbra Inc., Stryker European Operations BV, and Thrombolytic Science, LLC. J.G.J.V.A. reports grants or consulting fees from Boehringer-Ingelheim, MSD, BMS, AstraZeneca, Eli-Lilly, Verastem, Nutricia, Amphera, and CureVac. J.J.V. reports grants or consulting fees from Qure.ai, Tegus, Roche, participation on a data safety monitoring board or advisory board from Quibim, Contextflow, Noaber Foundation, and NLC Ventures; leadership or fiduciary roles on the steering committee of the PINPOINT Project (payment to institution from AstraZeneca) and RSNA Common Data Elements Steering Committee (unpaid); phantom shares in Contextflow and Quibim.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No complex statistical methods were necessary for this paper.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"No human 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":"The current study partially overlaps with the work by Van Leeuwen et al. [110], who framed the scientific evidence regarding AI in radiology within the RADAR framework, focusing on CE-certified AI technologies across various radiological subspecialties. Some of the studies included in our review may also have been part of their analysis, as their broader scope encompassed lung nodule evaluation. In contrast, our study specifically focuses on AI technologies for lung nodule evaluation, including both CE-certified and FDA-approved products, offering a more targeted and detailed analysis within this specific area. This overlap is also discussed in the Discussion section of our study, where we highlight how our research extends Van Leeuwen et al.\u2019s work by narrowing the focus to lung nodule evaluation.","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"}}]}}