[论文解读] Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools

Yingbo Ma, Yukyeong Song+13 more|arXiv (Cornell University)|Jul 10, 2024

Artificial Intelligence in Healthcare and Education被引用 10

一句话总结

对1,699篇文章（2016–2024）的范围性综述识别出18个AI教育框架和11个面向医学生的教学项目，并提出一个具有四个维度的AI素养框架，以在医学培训阶段定制教育。

ABSTRACT

As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.

研究动机与目标

• 随着AI驱动的医疗护理日益增多，激发医生对AI素养的需求。
• 评估AI目前在各课程与项目中的整合情况。
• 综合现有框架和教学示例，为未来课程提供指南。
• 突出在定义AI素养和提供有效教学方面的空白与挑战。

提出的方法

• 对2016年1月至2024年6月发表的1,699篇文章进行范围性综述。
• 识别出提出指导性框架的研究（n=18）以及记录真实世界教学的研究（n=11）。
• 分析内容以评估临床相关性、个性化以及与医学生发展轨迹的对齐程度。
• 在教学指南中发现差异，强调AI评估与伦理而非技术主题。
• 制定了一个具有四个维度的AI素养框架，以便将学习目标针对教育阶段进行定制。

实验结果

研究问题

• RQ1 针对医学生存在哪些AI教育框架，它们如何构建？
• RQ2 在医学教育文献中描述了哪些真实世界的AI教学？
• RQ3 在医学生的AI素养与课程设计中常见的空白、挑战与模糊之处有哪些？
• RQ4 如何定义并操作化AI素养以适应不同阶段的医学培训？

主要发现

• 18项研究提出用于医学情境的AI教育指导框架。
• 11项研究记录了在医学教育环境中实施的实际AI教学。
• 教学指南显示偏向AI评估与伦理，而非如数据科学和编码等技术主题。
• 指南存在显著差异，且对AI培训的临床价值缺乏公认证据。
• AI素养缺乏标准化定义且合格讲师短缺是关键障碍。
• 提出一个具有四个维度（Foundational, Practical, Experimental, Ethical）的AI素养框架，以在前临床、临床和临床研究阶段定制目标。