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Radiologists’ Usage of Diagnostic AI Systems

Kurzanzeige
dc.contributor.authorJussupow, Ekaterina
dc.contributor.authorSpohrer, Kai
dc.contributor.authorHeinzl, Armin
dc.date.accessioned2022-08-31T11:18:02Z
dc.date.available2022-08-31T11:18:02Z
dc.date.issued2021
dc.description.abstractWhile diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research.de
dc.identifier.doi10.1007/s12599-022-00750-2
dc.identifier.pissn1867-0202
dc.identifier.urihttp://dx.doi.org/10.1007/s12599-022-00750-2
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39315
dc.publisherSpringer
dc.relation.ispartofBusiness & Information Systems Engineering: Vol. 64, No. 3
dc.relation.ispartofseriesBusiness & Information Systems Engineering
dc.subjectArtificial intelligence
dc.subjectDecision making
dc.subjectExpert
dc.subjectMedicine
dc.subjectUsage
dc.titleRadiologists’ Usage of Diagnostic AI Systemsde
dc.typeText/Journal Article
gi.citation.endPage309
gi.citation.startPage293

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BISE 64(3) - June 2022