J. Lee is a third-year medical student, University of Toronto, Temerty Faculty of Medicine, Toronto, Ontario, Canada.
A.S. Wu is a third-year medical student, University of Toronto, Temerty Faculty of Medicine, Toronto, Ontario, Canada.
Acad Med. 2021 Nov 1;96(11S):S62-S70. doi: 10.1097/ACM.0000000000004291.
Artificial intelligence (AI) is a rapidly growing phenomenon poised to instigate large-scale changes in medicine. However, medical education has not kept pace with the rapid advancements of AI. Despite several calls to action, the adoption of teaching on AI in undergraduate medical education (UME) has been limited. This scoping review aims to identify gaps and key themes in the peer-reviewed literature on AI training in UME.
The scoping review was informed by Arksey and O'Malley's methodology. Seven electronic databases including MEDLINE and EMBASE were searched for articles discussing the inclusion of AI in UME between January 2000 and July 2020. A total of 4,299 articles were independently screened by 3 co-investigators and 22 full-text articles were included. Data were extracted using a standardized checklist. Themes were identified using iterative thematic analysis.
The literature addressed: (1) a need for an AI curriculum in UME, (2) recommendations for AI curricular content including machine learning literacy and AI ethics, (3) suggestions for curriculum delivery, (4) an emphasis on cultivating "uniquely human skills" such as empathy in response to AI-driven changes, and (5) challenges with introducing an AI curriculum in UME. However, there was considerable heterogeneity and poor consensus across studies regarding AI curricular content and delivery.
Despite the large volume of literature, there is little consensus on what and how to teach AI in UME. Further research is needed to address these discrepancies and create a standardized framework of competencies that can facilitate greater adoption and implementation of a standardized AI curriculum in UME.
人工智能（AI）是一种快速发展的现象，有可能引发医学领域的大规模变革。然而，医学教育并没有跟上 AI 的快速发展。尽管已经多次呼吁采取行动，但在本科医学教育（UME）中引入 AI 教学的情况仍然有限。本范围综述旨在确定 UME 中 AI 培训的同行评议文献中的差距和关键主题。
本范围综述以 Arksey 和 O'Malley 的方法为依据。对包括 MEDLINE 和 EMBASE 在内的七个电子数据库进行了搜索，以查找讨论在 UME 中纳入 AI 的文章，时间范围为 2000 年 1 月至 2020 年 7 月。共有 3 名共同研究者独立筛选了 4299 篇文章，其中 22 篇全文文章被纳入。使用标准化清单提取数据。通过迭代主题分析确定主题。
文献涉及：（1）在 UME 中需要 AI 课程，（2）AI 课程内容的建议，包括机器学习素养和 AI 伦理，（3）课程交付的建议，（4）强调培养“独特的人类技能”，例如应对 AI 驱动的变化的同理心，以及（5）在 UME 中引入 AI 课程的挑战。然而，关于 AI 课程内容和交付，研究之间存在很大的异质性和共识不足。
尽管文献数量庞大，但在 UME 中教授 AI 的内容和方法方面几乎没有共识。需要进一步研究来解决这些差异，并创建一个标准化的能力框架，以促进在 UME 中更广泛地采用和实施标准化的 AI 课程。
