为医学教育设计AI融合框架，助力培养未来医生的AI能力
AI-PACE: A Framework for Integrating AI into Medical Education
- 构建贯穿医学教育全程的AI教学框架
- 强调技术基础与临床应用并重的培养策略
- 适合医学教育者规划AI课程体系参考
人工智能在医疗领域的应用加速推进，但医学教育尚未跟上技术发展步伐。本文通过系统梳理文献，提炼出医学AI教育的关键能力、课程设计方法与实施路径，强调应在医学培养全周期中进行系统性AI教育，并提出一套可操作的课程建设框架。研究指出，有效的AI教育需贯穿整个医学训练过程，注重跨学科协作，平衡技术原理与临床应用场景。该框架为医学教育工作者提供理论支持与实践指导，助力培养适应智能化医疗环境的未来医师。
原文摘要 · Abstract (English)
The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.
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