Cited 4 times in
Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs
DC Field | Value | Language |
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dc.contributor.author | 김은경 | - |
dc.contributor.author | 신현주 | - |
dc.contributor.author | 이승수 | - |
dc.contributor.author | 김성원 | - |
dc.date.accessioned | 2023-03-27T02:59:14Z | - |
dc.date.available | 2023-03-27T02:59:14Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193757 | - |
dc.description.abstract | 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. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Public Library of Science | - |
dc.relation.isPartOf | PLOS ONE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Hospitals | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Physicians* | - |
dc.subject.MESH | Prospective Studies | - |
dc.subject.MESH | Software | - |
dc.title | Hospital-wide survey of clinical experience with artificial intelligence applied to daily chest radiographs | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Radiology (영상의학교실) | - |
dc.contributor.googleauthor | Hyun Joo Shin | - |
dc.contributor.googleauthor | Seungsoo Lee | - |
dc.contributor.googleauthor | Sungwon Kim | - |
dc.contributor.googleauthor | Nak-Hoon Son | - |
dc.contributor.googleauthor | Eun-Kyung Kim | - |
dc.identifier.doi | 10.1371/journal.pone.0282123 | - |
dc.contributor.localId | A00801 | - |
dc.contributor.localId | A02178 | - |
dc.contributor.localId | A05261 | - |
dc.relation.journalcode | J02540 | - |
dc.identifier.eissn | 1932-6203 | - |
dc.identifier.pmid | 36862644 | - |
dc.contributor.alternativeName | Kim, Eun Kyung | - |
dc.contributor.affiliatedAuthor | 김은경 | - |
dc.contributor.affiliatedAuthor | 신현주 | - |
dc.contributor.affiliatedAuthor | 이승수 | - |
dc.citation.volume | 18 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | e0282123 | - |
dc.identifier.bibliographicCitation | PLOS ONE, Vol.18(3) : e0282123, 2023-03 | - |
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