UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments.

Journal article

Rainey, C., O'Regan, T., Matthew, J., Skelton, E., Woznitza, N., Chu, K.-Y., Goodman, S., McConnell, J., Hughes, C., Bond, R., Malamateniou, C. and McFadden, S. 2022. UK reporting radiographers' perceptions of AI in radiographic image interpretation - Current perspectives and future developments. Radiography. 28 (4), pp. 881-888. https://doi.org/S1078-8174(22)00075-X
AuthorsRainey, C., O'Regan, T., Matthew, J., Skelton, E., Woznitza, N., Chu, K.-Y., Goodman, S., McConnell, J., Hughes, C., Bond, R., Malamateniou, C. and McFadden, S.
AbstractRadiographer reporting is accepted practice in the UK. With a national shortage of radiographers and radiologists, artificial intelligence (AI) support in reporting may help minimise the backlog of unreported images. Modern AI is not well understood by human end-users. This may have ethical implications and impact human trust in these systems, due to over- and under-reliance. This study investigates the perceptions of reporting radiographers about AI, gathers information to explain how they may interact with AI in future and identifies features perceived as necessary for appropriate trust in these systems. A Qualtrics® survey was designed and piloted by a team of UK AI expert radiographers. This paper reports the third part of the survey, open to reporting radiographers only. 86 responses were received. Respondents were confident in how an AI reached its decision (n = 53, 62%). Less than a third of respondents would be confident communicating the AI decision to stakeholders. Affirmation from AI would improve confidence (n = 49, 57%) and disagreement would make respondents seek a second opinion (n = 60, 70%). There is a moderate trust level in AI for image interpretation. System performance data and AI visual explanations would increase trust. Responses indicate that AI will have a strong impact on reporting radiographers' decision making in the future. Respondents are confident in how an AI makes decisions but less confident explaining this to others. Trust levels could be improved with explainable AI solutions. This survey clarifies UK reporting radiographers' perceptions of AI, used for image interpretation, highlighting key issues with AI integration. [Abstract copyright: Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.]
KeywordsClinical imaging; Radiography; Artificial intelligence; Education; Digital health; AI; Workforce training
Year2022
JournalRadiography
Journal citation28 (4), pp. 881-888
PublisherElsevier
ISSN1532-2831
Digital Object Identifier (DOI)https://doi.org/S1078-8174(22)00075-X
https://doi.org/10.1016/j.radi.2022.06.006
Official URLhttps://doi.org/10.1016/j.radi.2022.06.006
Publication dates
Online30 Jun 2022
Publication process dates
Accepted13 Jun 2022
Deposited18 Jul 2022
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Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer
Woznitza, N., Piper, K., Aladangady, N., Iliadis, K., Prakash, R. and Santos, R. 2014. Agreement in neonatal x-ray interpretation: a comparison between consultant paediatric radiologists and a reporting radiographer.
Advanced radiographic practice in adult chest imaging
Woznitza, N. 2014. Advanced radiographic practice in adult chest imaging.
Can reporting radiographers produce clinically relevant reports?
Woznitza, N. and Piper, K. 2011. Can reporting radiographers produce clinically relevant reports?