School of Dentistry & Medical Sciences, Charles Sturt University, Wagga Wagga, Australia.
School of Dentistry & Medical Sciences, Charles Sturt University, Port Macquarie, Australia.
J Med Radiat Sci. 2022 Sep;69(3):282-292. doi: 10.1002/jmrs.581. Epub 2022 Apr 15.
While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI.
An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de-identified from commencement.
Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico-legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity.
Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.
虽然人工智能（AI）和深度学习（DL）的最新发展激发了人们对医学影像的兴趣，但对于 AI 对影像技术人员的影响，几乎没有评论。本调查的目的是了解澳大利亚核医学和放射科专业人员对 AI 快速应用的态度、应用和关注点。
向农村核闪烁图联盟和澳大利亚医学影像学和放射治疗学会的核医学和放射科成员匿名在线调查，并邀请他们参与。调查邀请通过电子邮件发送给成员，并通过社交媒体推送，调查开放了 10 周。收集的所有信息都是匿名的，由于从一开始就对个人信息进行了去识别，因此不会披露个人信息。
在 102 名受访者中，对低阶任务（例如患者登记、分诊和配药）的接受程度很高，而对高阶任务自动化（例如手术和解释）的接受程度较低。在高阶任务（例如诊断、解释和决策制定）中，AI 的作用被认为优先级较低，而在自动化复杂任务（例如定量、分割、重建）或提高图像质量（例如剂量/噪声降低和用于衰减校正的伪 CT）的应用中，AI 的作用被认为优先级较高。医事法律、伦理、多样性和隐私问题引起了中度或高度关注，而对于 AI 具有临床实用性并提高效率似乎没有担忧。轻度关注包括冗余、培训偏差、透明度和有效性。
澳大利亚核医学技师和放射技师认识到 AI 在协助重复任务、执行较简单任务和提高医学影像输出质量方面的重要应用。存在与算法开发和实施的伦理方面相关的问题。
