增强放射技师的能力:呼吁在大学课程中纳入人工智能培训。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2024-03-08 eCollection Date: 2024-01-01 DOI:10.1155/2024/7001343
Mohammad A Rawashdeh, Sara Almazrouei, Maha Zaitoun, Praveen Kumar, Charbel Saade
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引用次数: 0

摘要

背景:人工智能(AI)的应用正在医学影像领域迅速发展。本研究旨在调查放射技师对人工智能的看法和知识:方法:采用谷歌表格进行在线调查,其中包括 20 个有关放射技师对人工智能认知的问题。问卷分为两部分。第一部分包括人口统计学信息、参与者是否认为人工智能应成为医学培训的一部分、他们以前对人工智能所用技术的了解以及他们是否愿意接受人工智能培训。问卷的第二部分包括两个栏目。第一部分包括16个问题,涉及放射技师对人工智能在放射学中应用的看法。我们采用了描述性分析和逻辑回归分析来评估性别对问卷项目的影响:对人工智能的熟悉程度很低,100 名受访者中只有 52 人(52%)表示对人工智能非常熟悉。许多参与者认为人工智能在医疗领域很有用(74%)。研究结果表明,几乎大多数参与者(98%)都认为应将人工智能纳入大学教育,其中 87% 的受访者倾向于接受人工智能培训,部分受访者已经对技术中使用的人工智能有所了解。逻辑回归分析表明,男性性别和 23-27 年的工作经验与对人工智能技术的熟悉程度之间存在显著关联,各自的几率比为 1.89(COR = 1.89)和 1.87(COR = 1.87):本研究表明,医疗机构对放射学领域的人工智能持积极态度。大多数受访者认为,人工智能应成为放射学教育的一部分。可能有必要为本科生和研究生放射技师提供人工智能培训课程,使他们为放射学发展中的人工智能工具做好准备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empowering Radiographers: A Call for Integrated AI Training in University Curricula.

Background: Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence.

Methods: An online survey employing Google Forms consisting of 20 questions regarding the radiographers' perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers' perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire.

Results: Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR = 1.89) and 1.87 (COR = 1.87).

Conclusions: This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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