{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:15:20Z","timestamp":1782947720900,"version":"3.54.5"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:00:00Z","timestamp":1608681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"University of Washington School of Pharmacy\u2019s financial support for recruitment"},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["T32 CA009168"],"award-info":[{"award-number":["T32 CA009168"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The PhRMA Foundation"},{"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\/100013382","name":"Institute of Translational Health Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100013382","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Center for Advancing Translational Science of the National Institutes of Health","award":["UL1 TR002319"],"award-info":[{"award-number":["UL1 TR002319"]}]},{"name":"National Center for Advancing Translational Science of the National Institutes of Health","award":["KL2 TR002317"],"award-info":[{"award-number":["KL2 TR002317"]}]},{"name":"National Center for Advancing Translational Science of the National Institutes of Health","award":["TL1 TR002318"],"award-info":[{"award-number":["TL1 TR002318"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,6,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is increasingly being proposed for use in medicine, including breast cancer screening (BCS). Little is known, however, about referring primary care providers\u2019 (PCPs\u2019) preferences for this technology.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We identified the most important attributes of AI BCS for ordering PCPs using qualitative interviews: sensitivity, specificity, radiologist involvement, understandability of AI decision-making, supporting evidence, and diversity of training data. We invited US-based PCPs to participate in an internet-based experiment designed to force participants to trade off among the attributes of hypothetical AI BCS products. Responses were analyzed with random parameters logit and latent class models to assess how different attributes affect the choice to recommend AI-enhanced screening.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Ninety-one PCPs participated. Sensitivity was most important, and most PCPs viewed radiologist participation in mammography interpretation as important. Other important attributes were specificity, understandability of AI decision-making, and diversity of data. We identified 3 classes of respondents: \u201cSensitivity First\u201d (41%) found sensitivity to be more than twice as important as other attributes; \u201cAgainst AI Autonomy\u201d (24%) wanted radiologists to confirm every image; \u201cUncertain Trade-Offs\u201d (35%) viewed most attributes as having similar importance. A majority (76%) accepted the use of AI in a \u201ctriage\u201d role that would allow it to filter out likely negatives without radiologist confirmation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions and Relevance<\/jats:title>\n                  <jats:p>Sensitivity was the most important attribute overall, but other key attributes should be addressed to produce clinically acceptable products. We also found that most PCPs accept the use of AI to make determinations about likely negative mammograms without radiologist confirmation.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa292","type":"journal-article","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T12:10:51Z","timestamp":1605096651000},"page":"1117-1124","source":"Crossref","is-referenced-by-count":46,"title":["Artificial intelligence in breast cancer screening: primary care provider preferences"],"prefix":"10.1093","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8154-0276","authenticated-orcid":false,"given":"Nathaniel","family":"Hendrix","sequence":"first","affiliation":[{"name":"The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington School of Pharmacy, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett","family":"Hauber","sequence":"additional","affiliation":[{"name":"The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington School of Pharmacy, Seattle, Washington, USA"},{"name":"RTI Health Solutions, Research Triangle Park, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph I","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA"},{"name":"Department of Health Services, University of Washington School of Public Health, Seattle, Washington, USA"},{"name":"Hutchinson Institute for Cancer Outcomes Research, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aasthaa","family":"Bansal","sequence":"additional","affiliation":[{"name":"The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington School of Pharmacy, Seattle, Washington, 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