Standalone AI in Breast Cancer Screening: Where We Are and What Is to Be Achieved

HomeRadiologyVol. 307, No. 5 PreviousNext Reviews and CommentaryEditorialStandalone AI in Breast Cancer Screening: Where We Are and What Is to Be AchievedAnabel M. Scaranelo Anabel M. Scaranelo Author AffiliationsFrom the Department of Medical Imaging, Faculty of Medicine, University of Toronto, 263 McCaul St, 4th Floor, Toronto, ON, Canada M5T 1W7.Address correspondence to the author (email: [email protected]).Anabel M. Scaranelo Published Online:May 23 2023https://doi.org/10.1148/radiol.230935MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin 2023;73(1):17–48. Crossref, Medline, Google Scholar2. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8):500–510. Crossref, Medline, Google Scholar3. Sarker IH. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput Sci 2021;2(6):420. Crossref, Medline, Google Scholar4. Geras KJ, Mann RM, Moy L. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology 2019;293(2):246–259. Link, Google Scholar5. Yoon JH, Strand F, Baltzer PAT, et al. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology 2023;307(5):e222639. Link, Google Scholar6. Hickman SE, Woitek R, Le EPV, et al. Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology 2022;302(1):88–104. Link, Google Scholar7. Freeman K, Geppert J, Stinton C, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021;374:n1872. Crossref, Medline, Google Scholar8. Uzun Ozsahin D, Ikechukwu Emegano D, Uzun B, Ozsahin I. The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis. Diagnostics (Basel) 2022;13(1):45. Crossref, Medline, Google Scholar9. Plato. Apology of Socrates. Translated by Benjamin Jowett. Oxford University Press, 1888. Project Gutenberg EBook. https://www.gutenberg.org/files/1656/1656-h/1656-h.htm. Accessed March 31, 2023. Google ScholarArticle HistoryReceived: Apr 12 2023Revision requested: Apr 19 2023Revision received: Apr 20 2023Accepted: Apr 20 2023Published online: May 23 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleStandalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-AnalysisMay 23 2023RadiologyRecommended Articles Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-AnalysisRadiology2023Volume: 307Issue: 5Artificial Intelligence Applied to Contrast-enhanced Mammography: Exploring Uncharted TerritoryRadiology2023Volume: 307Issue: 5Combining Deep Learning and Handcrafted Radiomics for Classification of Suspicious Lesions on Contrast-enhanced MammogramsRadiology2023Volume: 307Issue: 5Contrast-enhanced Mammography: Revisiting the Roots of ScreeningRadiology2023Volume: 307Issue: 5Effect of Human-AI Interaction on Detection of Malignant Lung Nodules on Chest RadiographsRadiology2023Volume: 307Issue: 5See More RSNA Education Exhibits RSNA Case Collection Vol. 307, No. 5 Metrics Altmetric Score PDF download