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Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports
INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078783/ https://www.ncbi.nlm.nih.gov/pubmed/35191186 http://dx.doi.org/10.1111/1754-9485.13388 |
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author | Lim, Sophie Soyeon Phan, Tuan D Law, Meng Goh, Gerard S Moriarty, Heather K Lukies, Matthew W Joseph, Timothy Clements, Warren |
author_facet | Lim, Sophie Soyeon Phan, Tuan D Law, Meng Goh, Gerard S Moriarty, Heather K Lukies, Matthew W Joseph, Timothy Clements, Warren |
author_sort | Lim, Sophie Soyeon |
collection | PubMed |
description | INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non‐radiologist clinicians' opinion on this topic. METHOD: Single‐centre online survey targeting non‐radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI‐generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI‐issued reports and perception of data privacy and security. RESULTS: Eighty‐eight responses were collected, including 47.9% of consultants. Non‐radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI‐issued reports compared with radiologist‐issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI‐hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non‐radiologist clinicians believed that medicolegal responsibility with errors in AI‐issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non‐radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001). CONCLUSION: A hybrid AI‐generated radiologist‐confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non‐radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting. |
format | Online Article Text |
id | pubmed-10078783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100787832023-04-07 Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports Lim, Sophie Soyeon Phan, Tuan D Law, Meng Goh, Gerard S Moriarty, Heather K Lukies, Matthew W Joseph, Timothy Clements, Warren J Med Imaging Radiat Oncol MEDICAL IMAGING INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non‐radiologist clinicians' opinion on this topic. METHOD: Single‐centre online survey targeting non‐radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI‐generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI‐issued reports and perception of data privacy and security. RESULTS: Eighty‐eight responses were collected, including 47.9% of consultants. Non‐radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI‐issued reports compared with radiologist‐issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI‐hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non‐radiologist clinicians believed that medicolegal responsibility with errors in AI‐issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non‐radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001). CONCLUSION: A hybrid AI‐generated radiologist‐confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non‐radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting. John Wiley and Sons Inc. 2022-02-21 2022-12 /pmc/articles/PMC10078783/ /pubmed/35191186 http://dx.doi.org/10.1111/1754-9485.13388 Text en © 2022 The Authors. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | MEDICAL IMAGING Lim, Sophie Soyeon Phan, Tuan D Law, Meng Goh, Gerard S Moriarty, Heather K Lukies, Matthew W Joseph, Timothy Clements, Warren Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title | Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title_full | Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title_fullStr | Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title_full_unstemmed | Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title_short | Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports |
title_sort | non‐radiologist perception of the use of artificial intelligence (ai) in diagnostic medical imaging reports |
topic | MEDICAL IMAGING |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078783/ https://www.ncbi.nlm.nih.gov/pubmed/35191186 http://dx.doi.org/10.1111/1754-9485.13388 |
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