{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T18:27:21Z","timestamp":1786991241603,"version":"build-2736575974"},"reference-count":36,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"am","delay-in-days":372,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["KL2 TR002317"],"award-info":[{"award-number":["KL2 TR002317"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["TL1 TR002318"],"award-info":[{"award-number":["TL1 TR002318"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UL1 TR002319"],"award-info":[{"award-number":["UL1 TR002319"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000048","name":"American Cancer Society","doi-asserted-by":"publisher","award":["CSDG-21-078-01-CPSH"],"award-info":[{"award-number":["CSDG-21-078-01-CPSH"]}],"id":[{"id":"10.13039\/100000048","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R37 CA240403"],"award-info":[{"award-number":["R37 CA240403"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007812","name":"University of Washington","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007812","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jacr.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American College of Radiology"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.jacr.2022.06.019","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T22:05:17Z","timestamp":1660341917000},"page":"1098-1110","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"title":["Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation"],"prefix":"10.1016","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8154-0276","authenticated-orcid":false,"given":"Nathaniel","family":"Hendrix","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn P.","family":"Lowry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joann G.","family":"Elmore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Lotter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Sorensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5168-070X","authenticated-orcid":false,"given":"William","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geraldine J.","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sana","family":"Parsian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne","family":"Kolb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arash","family":"Naeim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph I.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jacr.2022.06.019_bib1","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1093\/jnci\/djy222","article-title":"Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists","volume":"111","author":"Rodriguez-Ruiz","year":"2019","journal-title":"J\u00a0Natl Cancer Inst"},{"key":"10.1016\/j.jacr.2022.06.019_bib2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.jacr.2022.06.019_bib3","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1001\/jamaoncol.2020.3321","article-title":"External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms","volume":"6","author":"Salim","year":"2020","journal-title":"JAMA Oncol"},{"key":"10.1016\/j.jacr.2022.06.019_bib4","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.0265","article-title":"Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms","volume":"3","author":"Schaffter","year":"2020","journal-title":"JAMA Netw Open"},{"key":"10.1016\/j.jacr.2022.06.019_bib5","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1038\/s41591-020-01174-9","article-title":"Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach","volume":"27","author":"Lotter","year":"2021","journal-title":"Nat Med"},{"key":"10.1016\/j.jacr.2022.06.019_bib6","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1148\/radiol.2021203758","article-title":"Deep learning predicts interval and screening-detected cancer from screening mammograms: a case-case-control study in 6369 women","volume":"301","author":"Zhu","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2022.06.019_bib7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A\u00a0deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2022.06.019_bib8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/mp.13886","article-title":"Deep learning modeling using normal mammograms for predicting breast cancer risk","volume":"47","author":"Arefan","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.jacr.2022.06.019_bib9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1148\/radiol.2019190872","article-title":"Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction","volume":"294","author":"Dembrower","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.jacr.2022.06.019_bib10","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aba4373","article-title":"Toward robust mammography-based models for breast cancer risk","volume":"13","author":"Yala","year":"2021","journal-title":"Sci Transl Med"},{"key":"10.1016\/j.jacr.2022.06.019_bib11","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.semcancer.2020.06.002","article-title":"Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art","volume":"72","author":"Sechopoulos","year":"2021","journal-title":"Semin Cancer Biol"},{"key":"10.1016\/j.jacr.2022.06.019_bib12","first-page":"64","article-title":"Technology acceptance models in health informatics: TAM and UTAUT","volume":"263","author":"Ammenwerth","year":"2019","journal-title":"Stud Health Technol Inform"},{"key":"10.1016\/j.jacr.2022.06.019_bib13","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1056\/NEJMoa066099","article-title":"Influence of computer-aided detection on performance of screening mammography","volume":"356","author":"Fenton","year":"2007","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.jacr.2022.06.019_bib14","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1001\/jamainternmed.2015.5231","article-title":"Diagnostic accuracy of digital screening mammography with and without computer-aided detection","volume":"175","author":"Lehman","year":"2015","journal-title":"JAMA Intern Med"},{"key":"10.1016\/j.jacr.2022.06.019_bib15","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2214\/AJR.18.20392","article-title":"New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence","volume":"212","author":"Gao","year":"2019","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.jacr.2022.06.019_bib16","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1016\/j.jacr.2021.09.016","article-title":"Artificial intelligence support for mammography: in-practice clinical experience","volume":"18","author":"Tartar","year":"2021","journal-title":"J\u00a0Am Coll Radiol"},{"key":"10.1016\/j.jacr.2022.06.019_bib17","doi-asserted-by":"crossref","first-page":"48","DOI":"10.2214\/AJR.05.0215","article-title":"Computer-aided detection as evidence in the courtroom: potential implications of an appellate court\u2019s ruling","volume":"186","author":"Brenner","year":"2006","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.jacr.2022.06.019_bib18","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1136\/bmj.328.7436.360","article-title":"Discrete choice experiments in health care","volume":"328","author":"Ryan","year":"2004","journal-title":"BMJ"},{"key":"10.1016\/j.jacr.2022.06.019_bib19","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/hec.1697","article-title":"Discrete choice experiments in health economics: a review of the literature","volume":"21","author":"de Bekker-Grob","year":"2012","journal-title":"Health Econ"},{"key":"10.1016\/j.jacr.2022.06.019_bib20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jval.2012.08.2223","article-title":"Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force","volume":"16","author":"Johnson","year":"2013","journal-title":"Value Health"},{"key":"10.1016\/j.jacr.2022.06.019_bib21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1258\/135581907779497602","article-title":"Developing attributes and levels for discrete choice experiments using qualitative methods","volume":"12","author":"Coast","year":"2007","journal-title":"J\u00a0Health Serv Res Policy"},{"key":"10.1016\/j.jacr.2022.06.019_bib22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1177\/1098214005283748","article-title":"A\u00a0general inductive approach for analyzing qualitative evaluation data","volume":"27","author":"Thomas","year":"2006","journal-title":"Am J Eval"},{"key":"10.1016\/j.jacr.2022.06.019_bib23","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s40271-015-0118-z","article-title":"Sample size requirements for discrete-choice experiments in healthcare: a practical guide","volume":"8","author":"de Bekker-Grob","year":"2015","journal-title":"Patient"},{"key":"10.1016\/j.jacr.2022.06.019_bib24","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.jbi.2008.08.010","article-title":"A\u00a0metadata-driven methodology and workflow process for providing translational research informatics support","volume":"42","author":"Harris","year":"2009","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2022.06.019_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2019.103208","article-title":"The REDCap consortium: building an international community of software platform partners","volume":"95","author":"Harris","year":"2019","journal-title":"J\u00a0Biomed Inform"},{"key":"10.1016\/j.jacr.2022.06.019_bib26","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jval.2016.04.004","article-title":"Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR Conjoint Analysis Good Research Practices Task Force","volume":"19","author":"Hauber","year":"2016","journal-title":"Value Health"},{"key":"10.1016\/j.jacr.2022.06.019_bib27","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1007\/s11116-012-9394-9","article-title":"Can scale and coefficient heterogeneity be separated in random coefficients models?","volume":"39","author":"Hess","year":"2012","journal-title":"Transportation"},{"key":"10.1016\/j.jacr.2022.06.019_bib28","first-page":"385","article-title":"Psychometric methods in marketing research: part I, conjoint analysis","volume":"32","author":"Carroll","year":"1995","journal-title":"J\u00a0Mark Res"},{"key":"10.1016\/j.jacr.2022.06.019_bib29","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1002\/bimj.201700067","article-title":"Variable selection\u2014a review and recommendations for the practicing statistician","volume":"60","author":"Heinze","year":"2018","journal-title":"Biom J"},{"key":"10.1016\/j.jacr.2022.06.019_bib30","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1093\/jamia\/ocaa292","article-title":"Artificial intelligence in breast cancer screening: primary care provider preferences","volume":"28","author":"Hendrix","year":"2021","journal-title":"J\u00a0Am Med Inform Assoc"},{"key":"10.1016\/j.jacr.2022.06.019_bib31","first-page":"75","article-title":"American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography","volume":"57","author":"Saslow","year":"2007","journal-title":"CA Cancer J Clin"},{"issue":"CSCW","key":"10.1016\/j.jacr.2022.06.019_bib32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3359152","article-title":"The principles and limits of algorithm-in-the-loop decision making","volume":"3","author":"Green","year":"2019","journal-title":"Proc ACM Hum Comput Interact"},{"key":"10.1016\/j.jacr.2022.06.019_bib33","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/s41398-021-01224-x","article-title":"How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection","volume":"11","author":"Jacobs","year":"2021","journal-title":"Transl Psychiatry"},{"key":"10.1016\/j.jacr.2022.06.019_bib34","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1016\/j.jacr.2018.12.043","article-title":"A\u00a0qualitative study to understand patient perspective on the use of artificial intelligence in radiology","volume":"16","author":"Haan","year":"2019","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"1 Pt A","key":"10.1016\/j.jacr.2022.06.019_bib35","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jacr.2020.09.042","article-title":"Artificial intelligence in screening mammography: a population survey of women\u2019s preferences","volume":"18","author":"Ongena","year":"2021","journal-title":"J\u00a0Am Coll Radiol"},{"issue":"Suppl 1","key":"10.1016\/j.jacr.2022.06.019_bib36","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.1111\/1475-6773.12827","article-title":"Testing the impact of mixed-mode designs (mail and web) and multiple contact attempts within mode (mail or web) on clinician survey response","volume":"53","author":"Beebe","year":"2018","journal-title":"Health Serv Res"}],"container-title":["Journal of the American College of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S154614402200566X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S154614402200566X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T06:27:23Z","timestamp":1761546443000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S154614402200566X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":36,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["S154614402200566X"],"URL":"https:\/\/doi.org\/10.1016\/j.jacr.2022.06.019","relation":{},"ISSN":["1546-1440"],"issn-type":[{"value":"1546-1440","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation","name":"articletitle","label":"Article Title"},{"value":"Journal of the American College of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jacr.2022.06.019","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Published by Elsevier Inc. on behalf of American College of Radiology","name":"copyright","label":"Copyright"}]}}