Abstract
Introduction
Radiographer 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.
Aim
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.
Methods
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.
Results
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.
Conclusions
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.
Implications for practice
This survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.
Radiographer 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.
Aim
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.
Methods
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.
Results
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.
Conclusions
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.
Implications for practice
This survey clarifies UK reporting radiographers’ perceptions of AI, used for image interpretation, highlighting key issues with AI integration.
| Original language | English |
|---|---|
| Pages (from-to) | 881-888 |
| Number of pages | 8 |
| Journal | Radiography |
| Volume | 28 |
| Issue number | 4 |
| Early online date | 1 Jul 2022 |
| DOIs | |
| Publication status | Published (in print/issue) - 1 Nov 2022 |
Bibliographical note
Funding Information:The corresponding author (CR) is grateful to have recieved funding from the College of Radiographers Industry Partnership Scheme research award, CoRIPS 183.
Publisher Copyright:
© 2022 The Author(s)
Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Keywords
- Artificial Intelligence
- AI
- Radiography
- Education
- Workforce Training
- Digital Health
- Clinical Imaging
- Clinical imaging
- Workforce training
- Digital health
- Artificial intelligence
