Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications
ABSTRACT Introduction There is a growing adoption of artificial intelligence (AI) in the field of medical imaging. AI can potentially enhance patient care, improve workflow, and analyze patient's medical data. This study aimed to explore radiographers' knowledge, perceptions, and expectati...
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| Format: | Article |
| Language: | English |
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Wiley
2025-04-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70465 |
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| author | Asseel Khalaf Manar Alshammari Hawraa Zayed Maryam Emnawer Abdulmohsen Esfahani |
| author_facet | Asseel Khalaf Manar Alshammari Hawraa Zayed Maryam Emnawer Abdulmohsen Esfahani |
| author_sort | Asseel Khalaf |
| collection | DOAJ |
| description | ABSTRACT Introduction There is a growing adoption of artificial intelligence (AI) in the field of medical imaging. AI can potentially enhance patient care, improve workflow, and analyze patient's medical data. This study aimed to explore radiographers' knowledge, perceptions, and expectations toward integrating AI into medical imaging and to highlight one of the available applications of AI by evaluating an AI‐based software that generates chest reports. Methods A cross‐sectional survey was distributed to radiographers (n = 50) requesting information regarding demographics and knowledge of AI. In the retrospective part, chest radiographs were collected (n = 40), and an AI report was generated using Siemens AI software. A Likert scale was used by a radiologist to rate the report's accuracy. Ethical approval was obtained. Data are presented as mean ± SD. Results The survey results showed that most participants agreed that radiographers must adapt the AI technology, and they showed interest in taking courses about AI within radiography (98%, 92%, n = 50). Participants' opinions on AI correlated with their perceptions of AI education (p < 0.05, r = 0.307). The findings from the retrospective study showed that the radiologist agreed with 53% of the AI‐generated chest reports. Conclusion The study findings identified a need for AI education and training for radiographers to increase their knowledge and improve their ability to use AI. Additionally, the study demonstrated that AI‐powered tools are showing great promise in the field of medical imaging. |
| format | Article |
| id | doaj-art-db548327acf644d5a8bbfa3fe737e6d0 |
| institution | OA Journals |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Health Science Reports |
| spelling | doaj-art-db548327acf644d5a8bbfa3fe737e6d02025-08-20T02:11:03ZengWileyHealth Science Reports2398-88352025-04-0184n/an/a10.1002/hsr2.70465Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and ApplicationsAsseel Khalaf0Manar Alshammari1Hawraa Zayed2Maryam Emnawer3Abdulmohsen Esfahani4Radiologic Sciences Department, Faculty of Allied Health Sciences Kuwait University Kuwait City KuwaitDepartment of Radiology Al‐Sabah Hospital Kuwait City KuwaitDepartment of Radiology Jaber Al‐Ahmad Hospital Kuwait City KuwaitDepartment of Radiology Al‐Amiri Hospital Kuwait City KuwaitDepartment of Radiology Jaber Al‐Ahmad Hospital Kuwait City KuwaitABSTRACT Introduction There is a growing adoption of artificial intelligence (AI) in the field of medical imaging. AI can potentially enhance patient care, improve workflow, and analyze patient's medical data. This study aimed to explore radiographers' knowledge, perceptions, and expectations toward integrating AI into medical imaging and to highlight one of the available applications of AI by evaluating an AI‐based software that generates chest reports. Methods A cross‐sectional survey was distributed to radiographers (n = 50) requesting information regarding demographics and knowledge of AI. In the retrospective part, chest radiographs were collected (n = 40), and an AI report was generated using Siemens AI software. A Likert scale was used by a radiologist to rate the report's accuracy. Ethical approval was obtained. Data are presented as mean ± SD. Results The survey results showed that most participants agreed that radiographers must adapt the AI technology, and they showed interest in taking courses about AI within radiography (98%, 92%, n = 50). Participants' opinions on AI correlated with their perceptions of AI education (p < 0.05, r = 0.307). The findings from the retrospective study showed that the radiologist agreed with 53% of the AI‐generated chest reports. Conclusion The study findings identified a need for AI education and training for radiographers to increase their knowledge and improve their ability to use AI. Additionally, the study demonstrated that AI‐powered tools are showing great promise in the field of medical imaging.https://doi.org/10.1002/hsr2.70465AI applicationsartificial Intelligenceperceptionsradiography |
| spellingShingle | Asseel Khalaf Manar Alshammari Hawraa Zayed Maryam Emnawer Abdulmohsen Esfahani Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications Health Science Reports AI applications artificial Intelligence perceptions radiography |
| title | Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications |
| title_full | Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications |
| title_fullStr | Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications |
| title_full_unstemmed | Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications |
| title_short | Exploring Radiographers' Readiness for Artificial Intelligence in Kuwait: Insights and Applications |
| title_sort | exploring radiographers readiness for artificial intelligence in kuwait insights and applications |
| topic | AI applications artificial Intelligence perceptions radiography |
| url | https://doi.org/10.1002/hsr2.70465 |
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