Department of Radiology and Imaging Sciences, Emory University School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322.
Department of Radiology, Duke University Medical Center, Durham, North Carolina.
Acad Radiol. 2023 Jul;30(7):1481-1487. doi: 10.1016/j.acra.2023.01.005. Epub 2023 Jan 27.
To evaluate radiology residents' perspectives regarding inclusion of artificial intelligence/ machine learning (AI/ML) education in the residency curriculum.
An online anonymous survey was sent to 759 residents at 21 US radiology residency programs. Resident demographics, sub-specialty interests, educational background and research experiences, as well as the awareness, availability, and usefulness of various resources for AI/ML education were collected.
The survey response rate was 27% (209/759). A total of 74% of respondents were male, 80% were training at large university programs, and only a minority (<20) had formal education or research experience in AI/ML. All four years of training were represented (range: 20%-38%). The majority of the residents agreed or strongly agreed (83%) that AI/ML education should be a part of the radiology residency curriculum and that such education should equip them with the knowledge to troubleshoot an AI tool in practice / determine whether a tool is working as intended (82%). Among the residency programs that offer AI/ML education, the most common resources were lecture series (43%), national informatics courses (28%), and in-house/institutional courses (26%). About 24% of the residents reported no AI/ML educational offerings in their residency curriculum. Hands on AI/ML laboratory (67%) and lecture series (61%) were reported as the most beneficial or effective. The majority of the residents preferred AI/ML education offered as a continuous course spanning the radiology residency (R1 to R4) (76%), followed by mini fellowship during R4 (32%) and as a course during PGY1 (21%).
Residents largely favor the inclusion of formal AI/ML education in the radiology residency curriculum, prefer hands-on learning and lectures as learning tools, and prefer a continuous AI/ML course spanning R1-R4.
评估放射科住院医师对将人工智能/机器学习（AI/ML）教育纳入住院医师课程的看法。
向 21 家美国放射学住院医师培训计划中的 759 名住院医师发送了在线匿名调查。收集住院医师的人口统计学、专业兴趣、教育背景和研究经验，以及对 AI/ML 教育的各种资源的认识、可用性和实用性。
调查的回复率为 27%（209/759）。74%的受访者为男性，80%在大型大学项目中接受培训，只有少数（<20%）有 AI/ML 的正规教育或研究经验。所有四个培训年都有代表（范围：20%-38%）。大多数住院医师同意或强烈同意（83%），AI/ML 教育应该成为放射科住院医师课程的一部分，并且这种教育应该使他们具备在实践中解决 AI 工具问题/确定工具是否按预期工作的知识（82%）。在提供 AI/ML 教育的住院医师培训计划中，最常见的资源是讲座系列（43%）、国家信息学课程（28%）和内部/机构课程（26%）。约 24%的住院医师报告其住院医师课程中没有 AI/ML 教育课程。实践 AI/ML 实验室（67%）和讲座系列（61%）被报告为最有益或最有效的。大多数住院医师更喜欢将 AI/ML 教育作为贯穿放射科住院医师培训（R1 至 R4）的连续课程提供（76%），其次是 R4 期间的迷你奖学金（32%）和 PGY1 期间的课程（21%）。
住院医师在很大程度上支持将正式的 AI/ML 教育纳入放射科住院医师课程，他们更喜欢实践学习和讲座作为学习工具，并更喜欢贯穿 R1-R4 的连续 AI/ML 课程。
