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Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs
PURPOSE: To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. MATERIALS AND METHODS: A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest r...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980810/ https://www.ncbi.nlm.nih.gov/pubmed/36862644 http://dx.doi.org/10.1371/journal.pone.0282123 |
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author | Shin, Hyun Joo Lee, Seungsoo Kim, Sungwon Son, Nak-Hoon Kim, Eun-Kyung |
author_facet | Shin, Hyun Joo Lee, Seungsoo Kim, Sungwon Son, Nak-Hoon Kim, Eun-Kyung |
author_sort | Shin, Hyun Joo |
collection | PubMed |
description | PURPOSE: To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. MATERIALS AND METHODS: A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest radiographs was conducted with all clinicians and radiologists at our hospital in this prospective study. In our hospital, version 2 of the abovementioned software was utilized from March 2020 to February 2021 and could detect three types of lesions. Version 3 was utilized for chest radiographs by detecting nine types of lesions from March 2021. The participants of this survey answered questions on their own experience using AI-based software in daily practice. The questionnaires were composed of single choice, multiple choices, and scale bar questions. Answers were analyzed according to the clinicians and radiologists using paired t-test and the Wilcoxon rank-sum test. RESULTS: One hundred twenty-three doctors answered the survey, and 74% completed all questions. The proportion of individuals who utilized AI was higher among radiologists than clinicians (82.5% vs. 45.9%, p = 0.008). AI was perceived as being the most useful in the emergency room, and pneumothorax was considered the most valuable finding. Approximately 21% of clinicians and 16% of radiologists changed their own reading results after referring to AI, and trust levels for AI were 64.9% and 66.5%, respectively. Participants thought AI helped reduce reading times and reading requests. They answered that AI helped increase diagnostic accuracy and were more positive about AI after actual usage. CONCLUSION: Actual adaptation of AI for daily chest radiographs received overall positive feedback from clinicians and radiologists in this hospital-wide survey. Participating doctors preferred to use AI and regarded it more favorably after actual working with the AI-based software in daily clinical practice. |
format | Online Article Text |
id | pubmed-9980810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99808102023-03-03 Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs Shin, Hyun Joo Lee, Seungsoo Kim, Sungwon Son, Nak-Hoon Kim, Eun-Kyung PLoS One Research Article PURPOSE: To assess experience with and perceptions of clinical application of artificial intelligence (AI) to chest radiographs among doctors in a single hospital. MATERIALS AND METHODS: A hospital-wide online survey of the use of commercially available AI-based lesion detection software for chest radiographs was conducted with all clinicians and radiologists at our hospital in this prospective study. In our hospital, version 2 of the abovementioned software was utilized from March 2020 to February 2021 and could detect three types of lesions. Version 3 was utilized for chest radiographs by detecting nine types of lesions from March 2021. The participants of this survey answered questions on their own experience using AI-based software in daily practice. The questionnaires were composed of single choice, multiple choices, and scale bar questions. Answers were analyzed according to the clinicians and radiologists using paired t-test and the Wilcoxon rank-sum test. RESULTS: One hundred twenty-three doctors answered the survey, and 74% completed all questions. The proportion of individuals who utilized AI was higher among radiologists than clinicians (82.5% vs. 45.9%, p = 0.008). AI was perceived as being the most useful in the emergency room, and pneumothorax was considered the most valuable finding. Approximately 21% of clinicians and 16% of radiologists changed their own reading results after referring to AI, and trust levels for AI were 64.9% and 66.5%, respectively. Participants thought AI helped reduce reading times and reading requests. They answered that AI helped increase diagnostic accuracy and were more positive about AI after actual usage. CONCLUSION: Actual adaptation of AI for daily chest radiographs received overall positive feedback from clinicians and radiologists in this hospital-wide survey. Participating doctors preferred to use AI and regarded it more favorably after actual working with the AI-based software in daily clinical practice. Public Library of Science 2023-03-02 /pmc/articles/PMC9980810/ /pubmed/36862644 http://dx.doi.org/10.1371/journal.pone.0282123 Text en © 2023 Shin et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Shin, Hyun Joo Lee, Seungsoo Kim, Sungwon Son, Nak-Hoon Kim, Eun-Kyung Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title_full | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title_fullStr | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title_full_unstemmed | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title_short | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
title_sort | hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980810/ https://www.ncbi.nlm.nih.gov/pubmed/36862644 http://dx.doi.org/10.1371/journal.pone.0282123 |
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