Loi Su Jean, Ng Wenhui, Lai Christopher, Chua Eric Chern-Pin
Singapore Institute of Technology, 10 Dover Drive, 138683, Singapore.
Singapore Institute of Technology, 10 Dover Drive, 138683, Singapore.
J Med Imaging Radiat Sci. 2025 Mar;56(2):101798. doi: 10.1016/j.jmir.2024.101798. Epub 2024 Dec 22.
The rise of Artificial intelligence (AI) is reshaping healthcare, particularly in medical imaging. In this emerging field, clinical imaging personnel need proper training. However, formal AI education is lacking in medical curricula, coupled with a shortage of studies synthesising the availability of AI curricula tailored for clinical imaging personnel. This study therefore addresses the question "what are the current AI training programs or curricula for clinical imaging personnel?"
This review follows Arksey & O'Malley's framework and the PRISMA Extension for Scoping Reviews checklist. Six electronic databases were searched between June and September 2023 and the screening process comprised two stages. Data extraction was performed using a standardised charting form. Data was summarised in table format and thematically.
Twenty-two studies were included in this review. The goals of the curriculum include enhancing AI knowledge through the delivery of educational content and encouraging practical application and skills development in AI. The learning objectives comprise technical proficiency and model development, foundational knowledge and understanding, literature review and information utilisation, and practical application and problem-solving skills. Course content spanned nine areas, from fundamentals of AI to imaging informatics. Most curricula adopted an online mode of delivery, and the program duration varied significantly. All programs utilised didactic presentations, with several incorporating additional teaching methods and activities to fulfil curriculum goals. The target audiences and participants primarily involved radiology residents, while the creators and instructors comprised a multidisciplinary team of radiology and AI personnel. Various tools and resources were utilised, encompassing online courses and cloud-based notebooks. The curricula were well-received by participants, and time constraint emerged as a major challenge.
This scoping review provides an overview of the AI educational programs from existing literature to guide future developments in AI educational curricula. Future education efforts should prioritise evidence-based curriculum design, expand training offerings to radiographers, increase content offerings in imaging informatics, and effectively utilise different teaching strategies and training tools and resources in the curriculum.
人工智能（AI）的兴起正在重塑医疗保健领域，尤其是在医学成像方面。在这个新兴领域，临床成像人员需要适当的培训。然而，医学课程中缺乏正式的人工智能教育，同时也缺乏综合针对临床成像人员的人工智能课程可用性的研究。因此，本研究旨在解决“目前针对临床成像人员的人工智能培训项目或课程有哪些？”这一问题。
本综述遵循阿克斯西和奥马利的框架以及PRISMA扩展范围综述清单。于2023年6月至9月期间检索了六个电子数据库，筛选过程包括两个阶段。使用标准化图表形式进行数据提取。数据以表格形式进行总结，并按主题进行归纳。
本综述纳入了22项研究。课程目标包括通过提供教育内容来增强人工智能知识，并鼓励人工智能的实际应用和技能发展。学习目标包括技术熟练程度和模型开发、基础知识和理解、文献综述和信息利用，以及实际应用和解决问题的技能。课程内容涵盖九个领域，从人工智能基础到成像信息学。大多数课程采用在线授课模式，课程时长差异很大。所有课程都采用了教学演示，有几项还纳入了额外的教学方法和活动以实现课程目标。目标受众和参与者主要是放射科住院医师，而课程创建者和授课教师则由放射科和人工智能领域的多学科团队组成。使用了各种工具和资源，包括在线课程和基于云的笔记本。这些课程受到参与者的好评，时间限制成为一个主要挑战。
本范围综述概述了现有文献中的人工智能教育项目，以指导人工智能教育课程的未来发展。未来的教育工作应优先考虑基于证据的课程设计，扩大对放射技师的培训范围，增加成像信息学的内容，以及在课程中有效利用不同的教学策略和培训工具及资源。
