超越讲座:为人工智能时代重新想象精神病学教学
Laurent Elkrief1,2, Alexandre Hudon1,3,4,5, Giovanni Briganti6,7
1Département de Psychiatrie et d'Addictologie, Faculté de Médecine, Université de Montréal, Montréal, QC, Canada.
JMIR medical education
|October 31, 2025
概括
生成型人工智能 (AI) 需要重新思考医学教育. 这种观点建议从讲座转向人工智能辅助学习,案例研讨会和对人工智能成果的批判性评估,以促进精神病学中的临床推理.
科学领域:
- 医学教育 医学教育
- 精神病学培训 精神病学培训
- 医疗保健中的人工智能
背景情况:
- 由于诊断的模糊性和复杂性,传统的教学讲座对于精神病学来说是不够的.
- 生成型大语言模型 (LLM) 需要对当前的医学教育范式进行重新评估.
- 需要教育模型来培养复杂的临床推理,而不是刻意转移知识.
研究的目的:
- 为精神病学培训提出一个新的教育框架,集成人工智能 (AI).
- 从被动的知识传递转向积极的,促进了更高层次思维技能的发展.
- 为精神病学学员提供适应性推理框架,为人工智能媒介的未来提供.
主要方法:
- 将基础知识转移到异步的人工智能辅助微模块.
- 将同步会议转化为"模糊性研讨会",以基于案例的推理.
- 整合现场LLM关键交互演练,以便快速进行工程和输出评估.
- 重新调整评估 (例如,OSCE,反思性写作) 来评估临床推理.
主要成果:
- 拟议的模型强调积极的学习和临床智慧的发展.
- 它旨在培养复杂的临床推理和整合技能.
- 实施需要教师发展,机构投资和阶段性方法.
结论:
- 对于AI时代的精神病学学员来说,重新想象的教育方法是必不可少的.
- 这种模式促进了适应性推理和对AI工具的批判性评估.
- 成功的整合将提高精神病治疗的质量.
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