PDF(Pubmed)
Abstract：
BACKGROUND: The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians\' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.
OBJECTIVE: The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.
METHODS: We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.
RESULTS: Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.
CONCLUSIONS: This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.
UNASSIGNED: RR2-10.11124/JBIES-22-00374.
OBJECTIVE: The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.
METHODS: We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.
RESULTS: Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.
CONCLUSIONS: This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.
UNASSIGNED: RR2-10.11124/JBIES-22-00374.
摘要：
背景：将人工智能（AI）成功整合到临床实践中取决于医生对AI原理及其应用的理解。因此,医学教育课程必须纳入人工智能主题和概念，为未来的医生提供所需的基础知识和技能。然而,在为医学教育量身定制的结构化AI课程框架的当前理解和可用性方面存在知识差距，作为指导和促进学习过程的重要指南。
目的：本研究的总体目标是综合有关课程框架和当前教育计划的文献中的知识，这些文献侧重于医学生的AI教学和学习。居民,和执业医生。
方法：我们遵循了一个经过验证的框架和JoannaBriggsInstitute的范围审查方法指南。从2000年到2023年5月，信息专家在以下书目数据库中进行了全面搜索：MEDLINE（Ovid），Embase(Ovid),CENTRAL(CochraneLibrary),CINAHL（EBSCOhost），和Scopus以及灰色文学。论文仅限于英语和法语。这篇综述包括描述医学人工智能教学和学习课程框架的论文，不管国家。所有类型的论文和研究设计都包括在内，会议摘要和协议除外。两名审稿人独立筛选标题和摘要，阅读全文，并使用经过验证的数据提取表单提取数据。分歧通过协商一致解决，如果这是不可能的,征求了第三位审稿人的意见。我们遵守PRISMA-ScR（用于系统审查的首选报告项目和用于范围审查的Meta分析扩展）清单，以报告结果。
结果：在筛选的5104篇论文中，确定了21篇与我们的资格标准相关的论文。总的来说,90%(19/21)的论文总共描述了30个当前或以前提供的教育项目，10%(2/21)的论文描述了课程框架的要素。一个框架描述了在整个医学学习连续体中整合AI课程的一般方法，另一个框架描述了眼科AI的核心课程。没有论文描述理论，教育学,或指导教育计划的框架。
结论：这篇综述综合了医学教育领域AI课程框架和教育计划的最新进展。为了建立在这个基础上，鼓励未来的研究人员参与多学科的方法来重新设计课程。此外,鼓励就将人工智能纳入医学课程规划开展对话，并调查发展情况，部署,并评估这些创新的教育计划。
■RR2-10.11124/JBIES-22-00374。
目的：本研究的总体目标是综合有关课程框架和当前教育计划的文献中的知识，这些文献侧重于医学生的AI教学和学习。居民,和执业医生。
方法：我们遵循了一个经过验证的框架和JoannaBriggsInstitute的范围审查方法指南。从2000年到2023年5月，信息专家在以下书目数据库中进行了全面搜索：MEDLINE（Ovid），Embase(Ovid),CENTRAL(CochraneLibrary),CINAHL（EBSCOhost），和Scopus以及灰色文学。论文仅限于英语和法语。这篇综述包括描述医学人工智能教学和学习课程框架的论文，不管国家。所有类型的论文和研究设计都包括在内，会议摘要和协议除外。两名审稿人独立筛选标题和摘要，阅读全文，并使用经过验证的数据提取表单提取数据。分歧通过协商一致解决，如果这是不可能的,征求了第三位审稿人的意见。我们遵守PRISMA-ScR（用于系统审查的首选报告项目和用于范围审查的Meta分析扩展）清单，以报告结果。
结果：在筛选的5104篇论文中，确定了21篇与我们的资格标准相关的论文。总的来说,90%(19/21)的论文总共描述了30个当前或以前提供的教育项目，10%(2/21)的论文描述了课程框架的要素。一个框架描述了在整个医学学习连续体中整合AI课程的一般方法，另一个框架描述了眼科AI的核心课程。没有论文描述理论，教育学,或指导教育计划的框架。
结论：这篇综述综合了医学教育领域AI课程框架和教育计划的最新进展。为了建立在这个基础上，鼓励未来的研究人员参与多学科的方法来重新设计课程。此外,鼓励就将人工智能纳入医学课程规划开展对话，并调查发展情况，部署,并评估这些创新的教育计划。
■RR2-10.11124/JBIES-22-00374。
