评估放射科医生和放射技师对人工智能集成的看法：机遇与挑战。
Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.
机构信息
Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan.
Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan.
出版信息
Br J Radiol. 2024 Mar 28;97(1156):763-769. doi: 10.1093/bjr/tqae022.
OBJECTIVES
The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI.
METHODS
A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies.
RESULTS
Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts.
CONCLUSION
Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction.
ADVANCES IN KNOWLEDGE
Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
目的
本研究旨在评估放射科医生和放射技师对人工智能（AI）的意见和看法，以及其在放射科中的整合情况。此外，我们还调查了放射科医生和放射技师在了解 AI 时面临的最常见挑战和障碍。
方法
2023 年 5 月 29 日至 2023 年 7 月 30 日，我们向在医院和医疗中心工作的放射科医生和放射技师进行了一项全国性的在线描述性横断面调查。该问卷调查了参与者对 AI 及其在放射科中的应用的意见、感受和预测。使用描述性统计数据报告参与者的人口统计学和反应。五分制利克特量表数据使用发散堆叠条形图报告，以突出任何集中趋势。
结果
共收集了 258 名参与者的回复，结果显示他们对实施 AI 的态度积极。放射科医生和放射技师都预测乳腺成像将是受 AI 革命影响最大的亚专业。MRI、乳房 X 光摄影和 CT 被确定为 AI 应用领域的主要模态。放射科医生和放射技师在学习 AI 时遇到的主要障碍是缺乏专家的指导、指导和支持。
结论
参与者对学习 AI 和在放射科实践中实施 AI 表现出积极的态度。然而，放射科医生和放射技师在学习 AI 时遇到了一些障碍，例如缺乏经验丰富的专业人员的支持和指导。
知识进步
放射科医生和放射技师报告了 AI 学习的几个障碍，其中最大的障碍是缺乏专家的指导和指导，其次是缺乏资金和对新技术的投资。
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