Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

Abdulaziz A Qurashi,1 Rashed K Alanazi,1 Yasser M Alhazmi,1 Ahmed S Almohammadi,1 Walaa M Alsharif,1 Khalid M Alshamrani2,3 1Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia; 2College of Applied Medical Sciences, King Saud bin...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Qurashi AA, Alanazi RK, Alhazmi YM, Almohammadi AS, Alsharif WM, Alshamrani KM
Formato: article
Lenguaje:EN
Publicado: Dove Medical Press 2021
Materias:
artificial intelligence
ai-based applications
radiology
radiologists
imaging modalities
Medicine (General)
R5-920
Acceso en línea:https://doaj.org/article/52d13cfe772b48d09eae37b132b30a5e
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:52d13cfe772b48d09eae37b132b30a5e
record_format dspace
spelling oai:doaj.org-article:52d13cfe772b48d09eae37b132b30a5e2021-11-23T18:43:02ZSaudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study1178-2390https://doaj.org/article/52d13cfe772b48d09eae37b132b30a5e2021-11-01T00:00:00Zhttps://www.dovepress.com/saudi-radiology-personnels-perceptions-of-artificial-intelligence-impl-peer-reviewed-fulltext-article-JMDHhttps://doaj.org/toc/1178-2390Abdulaziz A Qurashi,1 Rashed K Alanazi,1 Yasser M Alhazmi,1 Ahmed S Almohammadi,1 Walaa M Alsharif,1 Khalid M Alshamrani2,3 1Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia; 2College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia; 3King Abdullah International Medical Research Center, Jeddah, Saudi ArabiaCorrespondence: Abdulaziz A QurashiDiagnostic Radiology Technology, Taibah University, Madinah, 42353, Saudi ArabiaTel +966 14 861 8888 Ext. 3603Email aaqurashi@taibahu.edu.saPurpose: Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel’s familiarity with AI applications and its usefulness in clinical practice were investigated.Methods: A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants’ knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants’ trust level was also measured; Kruskal–Wallis test was used to examine differences between groups.Results: A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).Conclusion: Radiology personnel’s knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.Keywords: artificial intelligence, AI-based applications, radiology, radiologists, imaging modalitiesQurashi AAAlanazi RKAlhazmi YMAlmohammadi ASAlsharif WMAlshamrani KMDove Medical Pressarticleartificial intelligenceai-based applicationsradiologyradiologistsimaging modalitiesMedicine (General)R5-920ENJournal of Multidisciplinary Healthcare, Vol Volume 14, Pp 3225-3231 (2021)
institution DOAJ
collection DOAJ
language EN
topic artificial intelligence
ai-based applications
radiology
radiologists
imaging modalities
Medicine (General)
R5-920
spellingShingle artificial intelligence
ai-based applications
radiology
radiologists
imaging modalities
Medicine (General)
R5-920
Qurashi AA
Alanazi RK
Alhazmi YM
Almohammadi AS
Alsharif WM
Alshamrani KM
Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
description Abdulaziz A Qurashi,1 Rashed K Alanazi,1 Yasser M Alhazmi,1 Ahmed S Almohammadi,1 Walaa M Alsharif,1 Khalid M Alshamrani2,3 1Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia; 2College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia; 3King Abdullah International Medical Research Center, Jeddah, Saudi ArabiaCorrespondence: Abdulaziz A QurashiDiagnostic Radiology Technology, Taibah University, Madinah, 42353, Saudi ArabiaTel +966 14 861 8888 Ext. 3603Email aaqurashi@taibahu.edu.saPurpose: Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel’s familiarity with AI applications and its usefulness in clinical practice were investigated.Methods: A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants’ knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants’ trust level was also measured; Kruskal–Wallis test was used to examine differences between groups.Results: A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).Conclusion: Radiology personnel’s knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.Keywords: artificial intelligence, AI-based applications, radiology, radiologists, imaging modalities
format article
author Qurashi AA
Alanazi RK
Alhazmi YM
Almohammadi AS
Alsharif WM
Alshamrani KM
author_facet Qurashi AA
Alanazi RK
Alhazmi YM
Almohammadi AS
Alsharif WM
Alshamrani KM
author_sort Qurashi AA
title Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
title_short Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
title_full Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
title_fullStr Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
title_full_unstemmed Saudi Radiology Personnel’s Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study
title_sort saudi radiology personnel’s perceptions of artificial intelligence implementation: a cross-sectional study
publisher Dove Medical Press
publishDate 2021
url https://doaj.org/article/52d13cfe772b48d09eae37b132b30a5e
work_keys_str_mv AT qurashiaa saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
AT alanazirk saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
AT alhazmiym saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
AT almohammadias saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
AT alsharifwm saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
AT alshamranikm saudiradiologypersonnelrsquosperceptionsofartificialintelligenceimplementationacrosssectionalstudy
_version_ 1718416154469335040

Ejemplares similares