Recalibrating AI in clinical decision-making: a process view of human–AI engagement in healthcare

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dc.contributor.authorKaja, Rania
dc.contributor.authorNeville, Karen
dc.contributor.authorTreacy, Stephen
dc.contributor.authorWoodworth, Simon
dc.date.accessioned2026-05-25T15:50:11Z
dc.date.available2026-05-25T15:50:11Z
dc.date.issued2026-04-30
dc.description.abstractThis study examines how clinicians continuously recalibrate when, how, and to what extent artificial intelligence (AI) shapes clinical decision-making, moving beyond static adoption and resistance models that treat engagement as a stable outcome. Based on 29 interviews across diverse clinical roles, our analysis indicates that clinicians do not hold stable positions of adoption or resistance. Instead, they enact five recurring decision orientations in practice: withdrawn, concealed, selective, cautious, and routine, through which they adjust the extent, visibility, and authority of AI in clinical judgement. These orientations reflect distinct decision logics grounded in situational assessments of risk, responsibility, and clinical context, and clinicians may shift between them as conditions change. By theorising AI engagement as an ongoing process of decision calibration rather than a one-time acceptance decision, this study offers a process-oriented explanation of human–AI engagement and outlines implications for the design, implementation, and governance of clinically accountable AI.en
dc.description.versionPublished Version
dc.format.extent15
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid2664787
dc.identifier.authororcidKaja, Rania
dc.identifier.authororcidNeville, Karen
dc.identifier.authororcidTreacy, Stephen§0000-0002-3980-5565
dc.identifier.authororcidWoodworth, Simon§0000-0001-7406-8743
dc.identifier.citationKaja, R, Neville, K, Treacy, S & Woodworth, S 2026, 'Recalibrating AI in clinical decision-making: a process view of human–AI engagement in healthcare', Journal of Decision Systems, vol. 35, no. 1, 2664787, pp. 1-15. https://doi.org/10.1080/12460125.2026.2664787
dc.identifier.doi10.1080/12460125.2026.2664787
dc.identifier.endpage15
dc.identifier.issn1246-0125
dc.identifier.issued1
dc.identifier.journaltitleJournal of Decision Systems
dc.identifier.otherRIS: urn:8D43B99627F8A0A2CD4842F6BDA30629
dc.identifier.otherORCID: /0000-0002-3980-5565/work/215732228
dc.identifier.otherORCID: /0000-0001-7406-8743/work/221645411
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/10468/18874
dc.identifier.volume35
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.relation.urihttps://www.scopus.com/pages/publications/105037628585
dc.relation.urihttps://www.scopus.com/pages/publications/105037628585?origin=resultslist
dc.rights© 2026, the Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
dc.rights.accessrightsopen access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectA-stable
dc.subjectAdoption model
dc.subjectArtificial intelligence
dc.subjectCalibration
dc.subjectClinical decision making
dc.subjectClinical decision-making
dc.subjectDecision calibration
dc.subjectDecision logic
dc.subjectDecision making
dc.subjectDecision orientation
dc.subjectDecision orientations
dc.subjectDecision theory
dc.subjectHealth care
dc.subjectHuman–AI engagement
dc.subjectHuman–artificial intelligence engagement
dc.subjectProcess-view
dc.subjectResistance models
dc.subjectRisk assessment
dc.subjectSituational assessment
dc.subject[CUBS]
dc.subjectdecision calibration
dc.subjectdecision orientations
dc.subjectclinical decision-making
dc.subjecthuman–AI engagement
dc.titleRecalibrating AI in clinical decision-making: a process view of human–AI engagement in healthcareen
dc.typeArticle (peer-reviewed)
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