{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T09:14:22Z","timestamp":1793610862589,"version":"4.5.0"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","ejradiology.com","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.ejrad.2024.111457","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T23:57:00Z","timestamp":1713311820000},"page":"111457","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":136,"special_numbering":"C","title":["Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects"],"prefix":"10.1016","volume":"175","author":[{"given":"Oliver","family":"D\u00edaz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandro","family":"Rodr\u00edguez-Ru\u00edz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9615-8205","authenticated-orcid":false,"given":"Ioannis","family":"Sechopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2024.111457_b0005","first-page":"214","article-title":"Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art","volume":"vol. 72","author":"Sechopoulos","year":"2021"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0010","doi-asserted-by":"crossref","first-page":"46","DOI":"10.7326\/M19-2125","article-title":"Breast cancer screening and diagnosis: a synopsis of the european breast guidelines","volume":"172","author":"Schunemann","year":"2020","journal-title":"Ann. Intern. Med."},{"issue":"9","key":"10.1016\/j.ejrad.2024.111457_b0015","doi-asserted-by":"crossref","first-page":"3262","DOI":"10.1007\/s00330-015-4175-4","article-title":"Double versus single reading of mammograms in a breast cancer screening programme: a cost-consequence analysis","volume":"26","author":"Posso","year":"2016","journal-title":"Eur. Radiol."},{"issue":"06","key":"10.1016\/j.ejrad.2024.111457_b0020","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1142\/S021800149300073X","article-title":"Image processing and computer aided diagnosis in digital mammography: a clinical perspective","volume":"7","author":"Pisano","year":"1993","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0025","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1177\/0284185118770917","article-title":"The efficacy of using computer-aided detection (cad) for detection of breast cancer in mammography screening: a systematic review","volume":"60","author":"Henriksen","year":"2019","journal-title":"Acta Radiol."},{"key":"10.1016\/j.ejrad.2024.111457_b0030","doi-asserted-by":"crossref","unstructured":"A. Rodriguez-Ruiz, J.-J. Mordang, N. Karssemeijer, I. Sechopoulos, R.M. Mann, Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?, in: 14th International Workshop on Breast Imaging (IWBI 2018), vol. 10718. SPIE, 2018, pp. 7\u201316.","DOI":"10.1117\/12.2317937"},{"issue":"4","key":"10.1016\/j.ejrad.2024.111457_b0035","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1109\/TMI.2019.2945514","article-title":"Deep neural networks improve radiologists\u2019 performance in breast cancer screening","volume":"39","author":"Wu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.ejrad.2024.111457_b0040","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1016\/S1470-2045(23)00298-X","volume":"24","author":"L\u00e5ng","year":"2023","journal-title":"Lancet Oncol."},{"issue":"3","key":"10.1016\/j.ejrad.2024.111457_b0045","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.rxeng.2020.11.005","article-title":"Are artificial intelligence systems useful in breast cancer screening programmes?","volume":"63","author":"Diaz","year":"2021","journal-title":"Radiolog\u0131a (English Edition)"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0050","first-page":"20190031","article-title":"The role of artificial intelligence in medical imaging research","volume":"2","author":"Tang","year":"2019","journal-title":"BJR\u2014 Open"},{"key":"10.1016\/j.ejrad.2024.111457_b0055","doi-asserted-by":"crossref","unstructured":"J. Li, J.-h. Cheng, J.-y. Shi, F. Huang, Brief introduction of back propagation (bp) neural network algorithm and its improvement, in: Advances in computer science and information engineering, Springer, 2012, pp. 553\u2013558.","DOI":"10.1007\/978-3-642-30223-7_87"},{"key":"10.1016\/j.ejrad.2024.111457_b0060","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.ejrad.2024.111457_b0065","unstructured":"C. Matsoukas, J.F. Haslum, M. Soderberg, K. Smith, Is it time to replace cnns with transformers for medical images? arXiv preprint arXiv:2108.09038, 2021."},{"key":"10.1016\/j.ejrad.2024.111457_b0070","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Proces. Syst."},{"issue":"5","key":"10.1016\/j.ejrad.2024.111457_b0075","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ejrad.2024.111457_b0080","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ejrad.2024.111457_b0085","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"9","key":"10.1016\/j.ejrad.2024.111457_b0090","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1093\/jnci\/djy222","article-title":"Standalone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists","volume":"111","author":"Rodriguez-Ruiz","year":"2019","journal-title":"JNCI: J. Natl. Cancer Inst."},{"key":"10.1016\/j.ejrad.2024.111457_b0095","series-title":"Cancer detection in relation to type and stage in the randomised Mammography Screening with Artificial Intelligence trial (MASAI)","author":"L\u00e5ng","year":"2024"},{"key":"10.1016\/j.ejrad.2024.111457_b0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103774","article-title":"Deep learning for mass detection in full field digital mammograms","volume":"121","author":"Agarwal","year":"2020","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"10.1016\/j.ejrad.2024.111457_b0105","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.6.3.031409","article-title":"Automatic mass detection in mammograms using deep convolutional neural networks","volume":"6","author":"Agarwal","year":"2019","journal-title":"J. Med. Imaging"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","article-title":"Deep learning to improve breast cancer detection on screening mammography","volume":"9","author":"Shen","year":"2019","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.ejrad.2024.111457_b0115","doi-asserted-by":"crossref","first-page":"pp","DOI":"10.1148\/ryai.2019180096","article-title":"Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis","volume":"1","author":"Conant","year":"2019","journal-title":"Radiol. Artif. Intell."},{"issue":"12","key":"10.1016\/j.ejrad.2024.111457_b0120","doi-asserted-by":"crossref","first-page":"6654","DOI":"10.1118\/1.4967345","article-title":"Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography","volume":"43","author":"Samala","year":"2016","journal-title":"Med. Phys."},{"issue":"4","key":"10.1016\/j.ejrad.2024.111457_b0125","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.diii.2019.02.008","article-title":"Detection and characterization of mri breast lesions using deep learning","volume":"100","author":"Herent","year":"2019","journal-title":"Diagn. Interv. Imaging"},{"key":"10.1016\/j.ejrad.2024.111457_b0130","doi-asserted-by":"crossref","unstructured":"M.A. Waller, S.E. Fawcett, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management, 2013, pp. 77\u201384.","DOI":"10.1111\/jbl.12010"},{"key":"10.1016\/j.ejrad.2024.111457_b0135","article-title":"The data revolution: Big data, open data, data infrastructures and their consequences","author":"Kitchin","year":"2014","journal-title":"Sage"},{"key":"10.1016\/j.ejrad.2024.111457_b0140","unstructured":"CrowdFlower, \u201cData Science,\u201d Tech. Rep., 2016. [Online]. Available: https:\/\/visit.figure-eight.com\/rs\/416-ZBE-142\/ images\/CrowdFlower DataScienceReport 2016.pdf."},{"key":"10.1016\/j.ejrad.2024.111457_b0145","doi-asserted-by":"crossref","unstructured":"O. Diaz, K. Kushibar, R. Osuala, A. Linardos, L. Garrucho, L. Igual, P. Radeva, F. Prior, P. Gkontra, K. Lekadir, Data preparation for artificial intelligence in medical imaging: a comprehensive guide to open-access platforms and tools, Physica medica, 83 (2021) 25\u201337.","DOI":"10.1016\/j.ejmp.2021.02.007"},{"key":"10.1016\/j.ejrad.2024.111457_b0150","doi-asserted-by":"crossref","unstructured":"B. Alyafi, O. Diaz, P. Elangovan, J.C. Vilanova, J. del Riego, R. Marti, Quality analysis of dcgan-generated mammography lesions, in: 15th International workshop on breast imaging (IWBI2020), vol. 11513. SPIE, 2020, pp. 80\u201385.","DOI":"10.1117\/12.2560473"},{"key":"10.1016\/j.ejrad.2024.111457_b0155","doi-asserted-by":"crossref","unstructured":"B. Alyafi, O. Diaz, R. Marti, Dcgans for realistic breast mass augmentation in x-ray mammography, in: Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314. SPIE, 2020, pp. 473\u2013480.","DOI":"10.1117\/12.2543506"},{"key":"10.1016\/j.ejrad.2024.111457_b0160","article-title":"Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging","author":"Osuala","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ejrad.2024.111457_b0165","doi-asserted-by":"crossref","unstructured":"Z. Szafranowska, R. Osuala, B. Breier, K. Kushibar, K. Lekadir, O. Diaz, Sharing generative models instead of private data: a simulation study on mammography patch classification, in: 16th International Workshop on Breast Imaging (IWBI2022), vol. 12286. SPIE, 2022, pp. 169\u2013177.","DOI":"10.1117\/12.2625781"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111457_b0170","doi-asserted-by":"crossref","first-page":"061403","DOI":"10.1117\/1.JMI.10.6.061403","article-title":"medigan: a python library of pretrained generative models for medical image synthesis","volume":"10","author":"Osuala","year":"2023","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.ejrad.2024.111457_b0175","doi-asserted-by":"crossref","first-page":"1044496","DOI":"10.3389\/fonc.2022.1044496","article-title":"High-resolution synthesis of high-density breast mammograms: application to improved fairness in deep learning based mass detection","volume":"12","author":"Garrucho","year":"2023","journal-title":"Front. Oncol."},{"issue":"4","key":"10.1016\/j.ejrad.2024.111457_b0180","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1007\/s00330-018-5601-1","article-title":"Medical students\u2019 attitude towards artificial intelligence: a multicentre survey","volume":"29","author":"Pinto Dos Santos","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2024.111457_b0185","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ejmp.2020.11.037","article-title":"Artificial intelligence in the medical physics community: an international survey","volume":"81","author":"Diaz","year":"2021","journal-title":"Phys. Med."},{"key":"10.1016\/j.ejrad.2024.111457_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2019.108742","article-title":"A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over","volume":"121","author":"van Hoek","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2024.111457_b0195","article-title":"A muggles guide to deep learning wizardry","author":"Currie","year":"2021","journal-title":"Radiography"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111457_b0200","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s00330-021-07892-z","article-title":"Artificial intelligence in radiology: 100 commercially available products and their scientific evidence","volume":"31","author":"van Leeuwen","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2024.111457_b0205","unstructured":"A. HLEG, Assessment list for trustworthy artificial intelligence (altai) for self-assessment, High Level Expert Group on Artificial Intelligence. B-1049 Brussels, 2020. [Online]. Available: https:\/\/digital-strategy.ec.europa.eu\/en\/library\/altai-self-assessment."},{"issue":"7","key":"10.1016\/j.ejrad.2024.111457_b0210","doi-asserted-by":"crossref","first-page":"e048008","DOI":"10.1136\/bmjopen-2020-048008","article-title":"Protocol for development of a reporting guideline (tripod-ai) and risk of bias tool (probast-ai) for diagnostic and prognostic prediction model studies based on artificial intelligence","volume":"11","author":"Collins","year":"2021","journal-title":"BMJ Open"},{"issue":"2","key":"10.1016\/j.ejrad.2024.111457_b0215","doi-asserted-by":"crossref","first-page":"pp","DOI":"10.1148\/ryai.2020200029","article-title":"Checklist for artificial intelligence in medical imaging (claim): a guide for authors and reviewers","volume":"2","author":"Mongan","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.ejrad.2024.111457_b0220","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the consort-ai extension","volume":"370","author":"Liu","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.ejrad.2024.111457_b0225","unstructured":"L. Maier-Hein, B. Menze et al., Metrics reloaded: Pitfalls and recommendations for image analysis validation, arXiv. org, no. 2206.01653, 2022."},{"key":"10.1016\/j.ejrad.2024.111457_b0230","unstructured":"K. Lekadir, A. Feragen, A.J. Fofanah, A.F. Frangi, A. Buyx, A. Emelie, A. Lara, A.R. Porras, A. Chan, A. Navarro, et al., FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare, arXiv preprint arXiv: 2309.12325, 2023."},{"key":"10.1016\/j.ejrad.2024.111457_b0235","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2022.102386","article-title":"Domain generalization in deep learning based mass detection in mammography: a large-scale multi-center study","volume":"132","author":"Garrucho","year":"2022","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0240","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2024.111457_b0245","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.artmed.2019.01.001","article-title":"Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach","volume":"94","author":"Lamy","year":"2019","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"10.1016\/j.ejrad.2024.111457_b0250","first-page":"pp","article-title":"\u201cDo we expect more from radiology ai than from radiologists?\u201d Radiology","volume":"3","author":"Mazurowski","year":"2021","journal-title":"Artif. Intell."},{"key":"10.1016\/j.ejrad.2024.111457_b0255","unstructured":"G. Vilone, L. Longo, Explainable artificial intelligence: a systematic review, arXiv preprint arXiv:2006.00093, 2020."},{"key":"10.1016\/j.ejrad.2024.111457_b0260","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"6","key":"10.1016\/j.ejrad.2024.111457_b0265","doi-asserted-by":"crossref","first-page":"e190208","DOI":"10.1148\/ryai.2020190208","article-title":"Improving breast cancer detection accuracy of mammography with the concurrent use of an artificial intelligence tool","volume":"2","author":"Pacile","year":"2020","journal-title":"Radiol. Artif. Intell."},{"issue":"2","key":"10.1016\/j.ejrad.2024.111457_b0270","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1148\/radiol.2018181371","article-title":"Detection of breast cancer with mammography: effect of an artificial intelligence support system","volume":"290","author":"Rodr\u0131guez-Ruiz","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2024.111457_b0275","doi-asserted-by":"crossref","unstructured":"S.L. van Winkel, A. Rodr\u0131guez-Ruiz, L. Appelman, A. Gubern-Merida, N. Karssemeijer, J. Teuwen, A.J. Wanders, I. Sechopoulos, R.M. Mann, Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study, Eur. Radiol., 31(11) (2021) 8682\u20138691.","DOI":"10.1007\/s00330-021-07992-w"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111457_b0280","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1148\/radiol.2021204432","article-title":"Impact of artificial intelligence decision support using deep learning on breast cancer screening interpretation with single-view wideangle digital breast tomosynthesis","volume":"300","author":"Pinto","year":"2021","journal-title":"Radiology"},{"issue":"3","key":"10.1016\/j.ejrad.2024.111457_b0285","doi-asserted-by":"crossref","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","article-title":"Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study","volume":"2","author":"Kim","year":"2020","journal-title":"The Lancet Digital Health"},{"key":"10.1016\/j.ejrad.2024.111457_b0290","doi-asserted-by":"crossref","DOI":"10.1016\/S2589-7500(23)00153-X","article-title":"Artificial intelligence for breast cancer detection in screening mammography in sweden: a prospective, population-based, paired-reader, noninferiority study","author":"Dembrower","year":"2023","journal-title":"The Lancet Digital Health"},{"issue":"12","key":"10.1016\/j.ejrad.2024.111457_b0295","doi-asserted-by":"crossref","first-page":"8238","DOI":"10.1007\/s00330-022-08909-x","article-title":"Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations","volume":"32","author":"Larsen","year":"2022","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0300","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12885-023-10890-7","article-title":"Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms","volume":"23","author":"Sharma","year":"2023","journal-title":"BMC Cancer"},{"issue":"7","key":"10.1016\/j.ejrad.2024.111457_b0305","doi-asserted-by":"crossref","first-page":"e507","DOI":"10.1016\/S2589-7500(22)00070-X","article-title":"Combining the strengths of radiologists and ai for breast cancer screening: a retrospective analysis","volume":"4","author":"Leibig","year":"2022","journal-title":"The Lancet Digital Health"},{"key":"10.1016\/j.ejrad.2024.111457_b0310","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s00330-020-07165-1","article-title":"Identifying normal mammograms in a large screening population using artificial intelligence","volume":"31","author":"L\u00e5ng","year":"2021","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0315","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1148\/radiol.2021203555","article-title":"Ai-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: a retrospective evaluation","volume":"300","author":"Raya-Povedano","year":"2021","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0320","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1148\/radiol.210948","article-title":"An artificial intelligence\u2013based mammography screening protocol for breast cancer: outcome and radiologist workload","volume":"304","author":"Lauritzen","year":"2022","journal-title":"Radiology"},{"issue":"9","key":"10.1016\/j.ejrad.2024.111457_b0325","doi-asserted-by":"crossref","first-page":"e468","DOI":"10.1016\/S2589-7500(20)30185-0","article-title":"Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study","volume":"2","author":"Dembrower","year":"2020","journal-title":"The Lancet Digital Health"},{"issue":"1","key":"10.1016\/j.ejrad.2024.111457_b0330","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1148\/radiol.211105","article-title":"Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis","volume":"303","author":"Shoshan","year":"2022","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.ejrad.2024.111457_b0335","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1001\/jamaoncol.2020.3321","article-title":"External evaluation of 3 commercial arti-ficial intelligence algorithms for independent assessment of screening mammograms","volume":"6","author":"Salim","year":"2020","journal-title":"JAMA Oncol."},{"key":"10.1016\/j.ejrad.2024.111457_b0340","unstructured":"Breast Cancer Surveillance Consortium, NCIfunded Breast Cancer Surveillance Consortium co-operative agreement, 2012, www.bcscresearch.org\/."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X24001736?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X24001736?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T15:21:37Z","timestamp":1729092097000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X24001736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":68,"alternative-id":["S0720048X24001736"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2024.111457","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2024.111457","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111457"}}