An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence

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dc.contributor.authorMcLaughlin, Lauraen
dc.contributor.authorMcFadden, Sonyia L.en
dc.contributor.authorVillikudathil, Angelina T.en
dc.contributor.authorMcConnell, Jonathanen
dc.contributor.authorHughes, Ciaraen
dc.contributor.authorBond, Raymonden
dc.contributor.authorRainey, Clareen
dc.contributor.funderCollege of Radiographersen
dc.date.accessioned2025-01-24T12:05:41Z
dc.date.available2025-01-24T12:05:41Z
dc.date.issued2024en
dc.description.abstractIntroduction Artificial intelligence (AI) is being used increasingly in image interpretation tasks. Human reliance on technology and bias can cause decision errors. A checklist, used with the AI to mitigate against such biases, may optimise the use of AI technologies and promote good decision hygiene. A checklist to aid radiographic image interpretation for radiographers using AI for image interpretation was formed. This study investigates the effect of a checklist for musculoskeletal (MSK) radiographic image assessment when using AI interpretive assistance. Methods Radiographers were asked to interpret five MSK examinations with AI feedback. They were then provided with the checklist and asked to reinterpret the same five examinations with the AI feedback (n = 140 interpretations). During the interpretation sessions, participants were asked to provide a diagnosis and a confidence level on the diagnosis provided. Participants were then asked to complete a questionnaire to gain feedback on the use of the checklist. Results Fourteen radiographers were recruited. Nine participants found the checklist alongside the AI most useful and five participants found the AI element to be most useful on its own. Five participants found the AI feedback to be useful as it helped to critique the radiographic image interpretation more closely and rethink their own initial diagnosis. Conclusion The checklist for use with AI in MSK image interpretation contained useful elements to the user, but further developments can be made to enhance its use in clinical practice.en
dc.description.sponsorshipCollege of Radiographers (Research IndustryPartnership Award Scheme (CoRIPS))en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleidjmrs.850en
dc.identifier.citationMcLaughlin, L., McFadden, S.L., Villikudathil, A.T., McConnell, J., Hughes, C., Bond, R. and Rainey, C. (2024) ‘An evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligence’, Journal of Medical Radiation Sciences, jmrs.850, (8pp). https://doi.org/10.1002/jmrs.850.en
dc.identifier.doihttps://doi.org/10.1002/jmrs.850en
dc.identifier.eissn2051-3909en
dc.identifier.endpage8en
dc.identifier.issn2051-3895en
dc.identifier.journaltitleJournal of Medical Radiation Sciencesen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16888
dc.language.isoenen
dc.publisherJohn Wiley & Sons Ltd.en
dc.rights© 2024, the Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf ofAustralian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.Checklist in image interpretation when using AI L. McLaughlin et al.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArtificial intelligenceen
dc.subjectChecklisten
dc.subjectImageinterpretationen
dc.subjectMusculoskeletalen
dc.titleAn evaluation of a checklist in musculoskeletal radiographic image interpretation when using artificial intelligenceen
dc.typeArticle (peer-reviewed)en
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