医学影像教育者对在医学影像课程中加入人工智能教育的看法：一项横断面调查

IF 2.8 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-09-15 DOI:10.1016/j.radi.2025.103153

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

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引用次数: 0

摘要

作为医疗保健中技术最先进的领域之一，人工智能（AI）对医学成像产生重大影响并不奇怪。卫生和护理专业委员会（HCPC）更新了熟练程度标准（SoP），要求临床医生证明对人工智能原则及其在实践中的应用的认识。影像课程必须更新，以确保专业人员达到这些标准。本研究探讨了医学影像教育工作者对人工智能教育的看法，考察了他们在提供人工智能内容方面的意识、态度和准备。方法开展了一项调查，以评估欧洲医学影像学课程中学术教育工作者的看法。该调查通过联合信息系统委员会（JISC）平台在线进行，包括封闭式和开放式问题。便利抽样用于2023年3月在维也纳举行的欧洲放射学大会（ECR）上招募与会者。结果共收到来自14个国家的33份回复。受访者来自诊断放射学背景（n = 21）或双重资格（n = 12）。只有15.1% （n = 5）完成了正式的人工智能培训。自我报告的准备程度表明，交付人工智能内容的准备程度低至中等。平均人工智能意识为9.21（可能的12），SD = 2.83。结论：虽然医学影像教育工作者表现出相对较高的人工智能意识，但他们提供人工智能内容的准备程度仍然较低。许多人表示，需要高等教育机构提供更大的支持，以确保工作人员有足够的能力将人工智能教育纳入医学成像课程。医学影像教育工作者对人工智能的认识与准备之间的差距强调了机构支持的必要性。高等教育机构应优先考虑以人工智能为重点的课程资源，使教育工作者更好地将人工智能融入医学影像教育，确保未来的放射技师培养必要的人工智能能力。

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Perception of medical imaging educators on the addition of AI education to the medical imaging curriculum: A cross-sectional survey

Introduction

As one of the most technologically advanced fields in healthcare, it is unsurprising that artificial intelligence (AI) is significantly impacting medical imaging. The Health and Care Professions Councils (HCPC) updated Standards of Proficiency (SoP) require clinicians to demonstrate awareness of AI principles and their application in practice. Imaging curricula must be updated to ensure professionals meet these standards. This study explores medical imaging educators’ perspectives on AI education, examining their awareness, attitudes, and preparedness to deliver AI content.

Methods

A survey was developed to assess the perceptions of academic educators in European medical imaging programmes. The survey was hosted online via the Joint Information Systems Committee (JISC) platform and included a mix of closed and open-ended questions. Convenience sampling was used to recruit attendees at the European Congress of Radiology (ECR) in Vienna in March 2023.

Results

A total of 33 responses were received from across 14 countries. Respondents were from a diagnostic radiography background (n = 21) or dual-qualified (n = 12). Only 15.1 % (n = 5) had completed formal AI training. Self-reported preparedness levels indicated a low to moderate preparedness to deliver AI content. Mean AI awareness was 9.21 (of a possible 12), SD = 2.83.

Conclusion

While medical imaging educators demonstrate relatively high AI awareness, their preparedness to deliver AI content remains low. Many expressed the need for greater support from higher education institutions (HEIs) to ensure staff are adequately equipped to integrate AI education into medical imaging curricula.

Implications for practice

The gap between AI awareness and preparedness among medical imaging educators underscores the need for institutional support. HEIs should prioritise AI-focused and curriculum resources to better equip educators in integrating AI into medical imaging education, ensuring future radiographers develop essential AI competencies.

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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.