It Takes More Than Enthusiasm: The Missing Infrastructure to Unlock AI's Potential in Medical Education.

IF 5.3 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Laurah Turner, Christine Zhou, Jesse Burk-Rafel
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引用次数: 0

Abstract

Abstract: Generative artificial intelligence (AI), including large language models (LLMs), is rapidly transforming health care delivery, yet medical education remains unprepared to harness its potential or mitigate its risks. While AI holds immense potential to enhance medical education, unguided adoption of these tools without proper educational frameworks risks undermining learners' clinical reasoning development and professional growth, as was seen with the electronic health record. In this commentary, the authors argue that the primary barrier to effective AI integration in medical education is not technological sophistication, but rather 3 critical infrastructure deficiencies: institutional implementation structures, sustainable funding mechanisms, and rigorous research methodologies. The authors propose establishing dedicated educational informatics teams with executive authority, creating targeted funding streams modeled after clinical research investments, and developing rigorous assessment frameworks with clear benchmarks for educational outcomes. Without these foundational elements, AI integration risks exacerbating inequities between institutions, potentially compromising physician development, and ultimately failing to improve patient care. Recommendations developed at a Macy Foundation conference on AI and Medical Education provide a roadmap for addressing these challenges, but significant infrastructural support is required to realize their potential. The authors argue that failure to address these structural gaps would perpetuate a cycle of innovation without implementation, a challenge that has plagued medical education for decades. In an era when AI is reshaping clinical practice daily, trainees cannot afford another well-intentioned but under-resourced educational transformation. Transformative educational change demands more than enthusiasm-it requires institutional commitment, significant investment, and methodological rigor commensurate with the high stakes of physician preparation.

需要的不仅仅是热情:缺少基础设施才能释放人工智能在医学教育中的潜力。
摘要:包括大型语言模型(llm)在内的生成式人工智能(AI)正在迅速改变医疗保健服务,但医学教育仍未准备好利用其潜力或降低其风险。虽然人工智能在加强医学教育方面具有巨大潜力,但在没有适当教育框架的情况下,未经指导地采用这些工具可能会损害学习者的临床推理发展和专业成长,正如电子健康记录所看到的那样。在这篇评论中,作者认为,将人工智能有效地整合到医学教育中的主要障碍不是技术的复杂性,而是3个关键的基础设施缺陷:机构实施结构、可持续的资助机制和严格的研究方法。作者建议建立具有行政权力的专门的教育信息学团队,以临床研究投资为模型创建有针对性的资金流,并制定具有明确教育成果基准的严格评估框架。如果没有这些基本要素,人工智能整合就有可能加剧机构之间的不平等,可能影响医生的发展,最终无法改善患者的护理。梅西基金会关于人工智能和医学教育的会议提出的建议为应对这些挑战提供了路线图,但需要大量基础设施支持才能发挥其潜力。这组作者认为,如果不能解决这些结构性差距,就会延续一个没有实施的创新循环,这是一个困扰医学教育几十年的挑战。在人工智能每天都在重塑临床实践的时代,学员们无法承受另一次善意但资源不足的教育转型。变革性的教育变革需要的不仅仅是热情——它需要制度上的承诺、大量的投资和与医生准备的高风险相称的严谨的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Academic Medicine
Academic Medicine 医学-卫生保健
CiteScore
7.80
自引率
9.50%
发文量
982
审稿时长
3-6 weeks
期刊介绍: Academic Medicine, the official peer-reviewed journal of the Association of American Medical Colleges, acts as an international forum for exchanging ideas, information, and strategies to address the significant challenges in academic medicine. The journal covers areas such as research, education, clinical care, community collaboration, and leadership, with a commitment to serving the public interest.
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