Balancing trust and reliance: Understanding the human-AI interaction to ensure responsible use of innovation and advanced technologies in radiography

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dc.check.date2027-05-19
dc.contributor.authorRainey, Clare
dc.contributor.editorHayre, Christopher
dc.contributor.editorDavidson, Rob
dc.contributor.editorChau, Shayne
dc.contributor.editorZheng, Xiaoming
dc.contributor.editorZhou, Abel
dc.contributor.editorFrame, Nigel
dc.date.accessioned2026-05-14T14:00:01Z
dc.date.available2026-05-14T14:00:01Z
dc.date.issued2026-05-19
dc.description.abstractAI is becoming ever more pervasive in healthcare. Radiography, as a technologically advanced profession, has experienced an influx of various AI-based assistive technologies which, due in part to government incentivisation, are being increasingly integrated into the clinical setting. In the UK, revisions to the Health and Care Professions Council’s (HCPC) Standards of Proficiency (SoP), valid from September 2023, place a requirement on registered radiographers to ‘demonstrate awareness of the principles of AI and deep learning technology, and its application to practice’ (standard 12.25). Whilst the radiology and radiography professions have been accustomed to adopting new technologies, the advent of deep learning has presented new challenges for even the technologically proficient user, and more concerning challenges may exist for those for whom clinical AI and deep learning technologies remain areas which require further exploration and understanding. This chapter aims to clarify barriers to the responsible use of AI in the clinical setting related to the human interaction with advanced systems and address issues of potential over- and underreliance on emerging technologies. It will introduce the reader to both ‘sides of the coin’ - how to ensure appropriate trust relating to the technology used and how this might be achieved, leading to improved human-computer interaction, with a focus on the vital roles which radiographers may play in responsible technology use and acceptance.en
dc.description.versionAccepted Version
dc.format.extent13
dc.format.mimetypeapplication/pdfen
dc.identifier.authororcidRainey, Clare§0000-0003-0449-8646
dc.identifier.authororcidHayre, Christopher
dc.identifier.authororcidDavidson, Rob
dc.identifier.authororcidChau, Shayne
dc.identifier.authororcidZheng, Xiaoming
dc.identifier.authororcidZhou, Abel
dc.identifier.authororcidFrame, Nigel
dc.identifier.citationRainey, C 2026, Balancing trust and reliance: Understanding the human-AI interaction to ensure responsible use of innovation and advanced technologies in radiography. in C Hayre, R Davidson, S Chau, X Zheng, A Zhou & N Frame (eds), Artifcial Intelligence and Data Analytics in Medical Imaging. CRC Press, pp. 81-93. https://doi.org/10.1201/9781003394068-4
dc.identifier.doi10.1201/9781003394068-4
dc.identifier.endpage93
dc.identifier.isbn9781032494913
dc.identifier.isbn9781040497517
dc.identifier.otherORCID: /0000-0003-0449-8646/work/214723707
dc.identifier.startpage81
dc.identifier.urihttps://hdl.handle.net/10468/18761
dc.language.isoen
dc.publisherCRC Press
dc.relation.ispartofArtifcial Intelligence and Data Analytics in Medical Imaging
dc.relation.urihttps://www.scopus.com/pages/publications/105037301566
dc.rights© 2026, selection and editorial matter, Christopher M. Hayre, Rob Davidson, Shayne Chau, Xiaoming Zheng, Abel Zhou, and Nigel Frame; individual chapters, the contributors.
dc.rights.accessrightsembargoed access
dc.rights.licensenameAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusPeer reviewed
dc.subjectAI
dc.subjectHealthcare
dc.subjectRadiography
dc.subject[Medicine]
dc.titleBalancing trust and reliance: Understanding the human-AI interaction to ensure responsible use of innovation and advanced technologies in radiographyen
dc.typeBook chapter
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