School of Dentistry & Medical Sciences, Charles Sturt University, Wagga Wagga, Australia.
College of Veterinary Medicine, University of Tennessee, Knoxville, Tennessee, USA.
Vet Radiol Ultrasound. 2023 May;64(3):473-483. doi: 10.1111/vru.13234. Epub 2023 Apr 6.
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 the veterinarian and veterinary imaging technologists. This survey study aimed to understand the attitudes, applications, and concerns among veterinarians and radiography professionals in Australia regarding the rapidly emerging applications of AI. An anonymous online survey was circulated to the members of three Australian veterinary professional organizations. The survey invitations were shared via email and social media with the survey open for 5 months. Among the 84 respondents, there was a high level of acceptance of lower order tasks (e.g., patient registration, triage, 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 veterinarians and veterinary professionals recognize 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) 的最新发展引起了人们对医学成像的兴趣，但关于 AI 对兽医和兽医影像技术人员的影响的评论却很少。这项调查研究旨在了解澳大利亚兽医和放射科专业人员对 AI 快速发展的应用的态度、应用和关注点。一项匿名在线调查分发给了三个澳大利亚兽医专业组织的成员。调查邀请通过电子邮件和社交媒体分享，调查开放了 5 个月。在 84 名受访者中，对低阶任务（例如，患者登记、分诊和配药）的接受程度较高，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）中，AI 的作用优先级较低，而在那些自动化复杂任务（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，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 AI 具有临床实用性并提高效率的问题似乎没有关注。轻度关注包括冗余、培训偏差、透明度和有效性。澳大利亚兽医和兽医专业人员认识到 AI 在协助重复任务、执行较简单任务和提高医学成像输出质量方面的重要应用。人们对算法开发和实施的伦理方面存在一些担忧。
