Doherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S
Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
Ulster University, School of Health Sciences, Faculty of Life and Health Sciences, Shore Road, Newtownabbey, Northern Ireland, United Kingdom.
Radiography (Lond). 2024 Dec;30 Suppl 2:79-87. doi: 10.1016/j.radi.2024.10.010. Epub 2024 Oct 30.
In Autumn 2023, amendments to the Health and Care Professions Councils (HCPC) Standards of Proficiency for Radiographers were introduced requiring clinicians to demonstrate awareness of the principles of AI and deep learning technology, and its application to practice' (HCPC 2023; standard 12.25). With the rapid deployment of AI in departments, staff must be prepared to implement and utilise AI. AI readiness is crucial for adoption, with education as a key factor in overcoming fear and resistance. This survey aimed to assess the current understanding of AI among students and qualified staff in clinical practice.
A survey targeting radiographers (diagnostic and therapeutic), radiologists and students was conducted to gather demographic data and assess awareness of AI in clinical practice. Hosted online via JISC, the survey included both closed and open-ended questions and was launched in March 2023 at the European Congress of Radiology (ECR).
A total of 136 responses were collected from participants across 25 countries and 5 continents. The majority were diagnostic radiographers 56.6 %, followed by students 27.2 %, dual-qualified 3.7 % and radiologists 2.9 %. Of the respondents, 30.1 % of respondents indicated that their highest level of qualification was a Bachelor's degree, 29.4 % stated that they are currently using AI in their role, whilst 27 % were unsure. Only 10.3 % had received formal AI training.
This study reveals significant gaps in training and understanding of AI among medical imaging staff. These findings will guide further research into AI education for medical imaging professionals.
This paper lays foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.
2023年秋季，对健康与护理专业委员会（HCPC）放射技师能力标准进行了修订，要求临床医生了解人工智能和深度学习技术的原理及其在实践中的应用（HCPC，2023；标准12.25）。随着人工智能在各科室的迅速应用，工作人员必须做好实施和利用人工智能的准备。人工智能准备情况对于采用人工智能至关重要，而教育是克服恐惧和抵触情绪的关键因素。本次调查旨在评估临床实践中，学生和合格工作人员对人工智能的当前理解。
针对放射技师（诊断和治疗）、放射科医生和学生开展了一项调查，以收集人口统计学数据，并评估他们在临床实践中对人工智能的认知。该调查通过JISC在线进行，包括封闭式和开放式问题，于2023年3月在欧洲放射学大会（ECR）上启动。
共收集到来自25个国家和5大洲参与者的136份回复。大多数是诊断放射技师，占56.6%，其次是学生，占27.2%，双资格人员占3.7%，放射科医生占2.9%。在受访者中，30.1%的受访者表示其最高学历为学士学位，29.4%表示他们目前在工作中使用人工智能，而27%不确定。只有10.3%接受过正式的人工智能培训。
本研究揭示了医学影像工作人员在人工智能培训和理解方面存在重大差距。这些发现将为进一步研究医学影像专业人员的人工智能教育提供指导。
本文为未来关于为医学影像专业人员提供人工智能教育的定性研究奠定了基础，有助于让工作人员为人工智能在医学影像中不断演变的作用做好准备。
