放射技师人工智能教育与学习（REAL-AI）：放射技师、放射医师和学生对人工智能教育的认识和态度调查

IF 2.5 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2024-10-30 DOI:10.1016/j.radi.2024.10.010

G. Doherty , L. McLaughlin , C. Hughes , J. McConnell , R. Bond , S. McFadden

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引用次数: 0

摘要

引言 2023 年秋季，卫生与护理专业委员会（HCPC）对《放射技师能力标准》进行了修订，要求临床医师展示对人工智能和深度学习技术原理及其在实践中应用的认识"（HCPC 2023；标准 12.25）。随着人工智能在各科室的快速部署，工作人员必须做好实施和使用人工智能的准备。人工智能的准备程度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。方法针对放射技师（诊断和治疗）、放射医师和学生开展了一项调查，以收集人口统计数据并评估临床实践中对人工智能的认识。调查通过监委会进行在线主持，包括封闭式问题和开放式问题，于2023年3月在欧洲放射学大会（ECR）上启动。结果共收集到来自5大洲25个国家参与者的136份回复。大多数人是放射诊断技师，占 56.6%，其次是学生，占 27.2%，具有双重资格的占 3.7%，放射科医生占 2.9%。30.1%的受访者表示他们的最高学历是学士学位，29.4%的受访者表示他们目前正在工作中使用人工智能，27%的受访者表示不确定。只有 10.3% 的人接受过正规的人工智能培训。本论文为今后对医学影像专业人员进行人工智能教育的定性研究奠定了基础，有助于为人工智能在医学影像领域不断发展的作用做好准备。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI

Introduction

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.

Methods

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).

Results

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.

Conclusion

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.

Implications for practice

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.

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.