Brigham and Women's Department of Emergency Medicine, 75 Francis St.Neville House, Boston, MA 02115.
UC Irvine School of Medicine, Irvine, California.
Acad Radiol. 2022 May;29 Suppl 5:S70-S75. doi: 10.1016/j.acra.2021.03.023. Epub 2021 May 18.
Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model.
放射学教育被认为是医学院校和住院医师培训的重要组成部分,但在教学指导方面缺乏标准化。放射学教学和学习方式的不一致性为新技术的介入提供了机会。现在,随着人工智能在医学领域的融合,当前的医学实习生课程很可能会受到其影响,无论是在教育还是医疗实践方面。本文旨在探讨当前医学实习生课程中的放射学教育现状,并了解人工智能如何可能对当前和未来的放射学教育模式产生影响。