Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida.
Department of Radiology, University of Miami Miller School of Medicine, Miami, Florida.
J Am Coll Radiol. 2018 Dec;15(12):1753-1757. doi: 10.1016/j.jacr.2017.12.021. Epub 2018 Feb 21.
Advances in artificial intelligence applied to diagnostic radiology are predicted to have a major impact on this medical specialty. With the goal of establishing a baseline upon which to build educational activities on this topic, a survey was conducted among trainees and attending radiologists at a single residency program.
An anonymous questionnaire was distributed. Comparisons of categorical data between groups (trainees and attending radiologists) were made using Pearson χ analysis or an exact analysis when required. Comparisons were made using the Wilcoxon rank sum test when the data were not normally distributed. An α level of 0.05 was used.
The overall response rate was 66% (69 of 104). Thirty-six percent of participants (n = 25) reported not having read a scientific medical article on the topic of artificial intelligence during the past 12 months. Twenty-nine percent of respondents (n = 12) reported using artificial intelligence tools during their daily work. Trainees were more likely to express doubts on whether they would have pursued diagnostic radiology as a career had they known of the potential impact artificial intelligence is predicted to have on the specialty (P = .0254) and were also more likely to plan to learn about the topic (P = .0401).
Radiologists lack exposure to current scientific medical articles on artificial intelligence. Trainees are concerned by the implications artificial intelligence may have on their jobs and desire to learn about the topic. There is a need to develop educational resources to help radiologists assume an active role in guiding and facilitating the development and implementation of artificial intelligence tools in diagnostic radiology.
人工智能在诊断放射学中的应用进展预计将对这一医学专业产生重大影响。为了在此基础上建立关于该主题的教育活动的基线，对一个单一的住院医师培训计划中的受训者和主治放射科医生进行了调查。
采用匿名问卷进行调查。采用 Pearson χ 检验或确切分析比较组间（受训者和主治放射科医生）的分类数据，当数据不服从正态分布时，采用 Wilcoxon 秩和检验进行比较。采用 α 水平 0.05。
总的回复率为 66%（104 名中的 69 名）。36%的参与者（n=25）报告在过去 12 个月内没有阅读过关于人工智能主题的科学医学文章。29%的受访者（n=12）报告在日常工作中使用人工智能工具。受训者更有可能对他们是否会选择从事诊断放射学职业表示怀疑，如果他们知道人工智能对该专业的潜在影响（P=0.0254），并且更有可能计划学习该主题（P=0.0401）。
放射科医生缺乏对当前人工智能科学医学文章的接触。受训者对人工智能可能对其工作产生的影响感到担忧，并希望了解该主题。有必要开发教育资源，帮助放射科医生在指导和促进人工智能工具在诊断放射学中的开发和实施方面发挥积极作用。
