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dc.contributor.authorEllingsen, Gunnar
dc.contributor.authorSilsand, Line
dc.contributor.authorSeverinsen, Gro-Hilde
dc.contributor.authorLinstad, Line Helen
dc.date.accessioned2022-11-24T11:09:59Z
dc.date.available2022-11-24T11:09:59Z
dc.date.issued2022
dc.description.abstractArtificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging examinations. However, the implementation of AI for clinical practice has not lived up to expectations. We suggest that a key problem with AI projects in radiology is that high expectations associated with new and unproven AI technology tend to scale the projects in ways that challenge their anchoring in local practice and their initial purpose of serving local needs. Empirically, we focus on the procurement of an AI solution for radiology practice at a large health trust in Norway where it was intended that AI technology would be used to process the screening of images more effectively. Theoretically, we draw on the information infrastructure literature, which is concerned with scaling innovative technologies from local settings, with a limited number of users, to broad-use contexts with many users.en_US
dc.identifier.citationEllingsen G, Silsand L, Severinsen G-H, Linstad LH. Scaling AI Projects for Radiology– Causes and Consequences. Studies in Health Technology and Informatics. 2022en_US
dc.identifier.cristinIDFRIDAID 2047878
dc.identifier.doi10.3233/SHTI220387
dc.identifier.issn0926-9630
dc.identifier.issn1879-8365
dc.identifier.urihttps://hdl.handle.net/10037/27520
dc.language.isoengen_US
dc.publisherIOS Pressen_US
dc.relation.ispartofseriesStudies in Health Technology and Informatics; 294en_US
dc.relation.journalStudies in Health Technology and Informatics
dc.relation.projectIDNorges forskningsråd: 188932en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleScaling AI Projects for Radiology– Causes and Consequencesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)