An evaluation of information online on artificial intelligence in medical imaging

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Mulryan, Philip
Ni Chleirigh, Naomi
O'Mahony, Alexander T.
Crowley, Claire
Ryan, David
McLaughlin, Patrick
McEntee, Mark F.
Maher, Michael
O'Connor, Owen J.
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Springer Nature Switzerland AG
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Background: Opinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse. Methods: Two search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria. Results: Of the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%). Conclusion: The overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment.
AI , Radiology , Artificial intelligence in radiology , Perspectives on evolution of radiology , Future impact on the radiologist , Radiology recruitment , Radiology efficiency
Mulryan, P., Ni Chleirigh, N., O’Mahony, A. T., Crowley, C., Ryan, D., McLaughlin, P., McEntee, M., Maher, M. and O’Connor, O. J. (2022) ‘An evaluation of information online on artificial intelligence in medical imaging’, Insights into Imaging 13, 79 (11pp). doi: 10.1186/s13244-022-01209-4
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