人工智能融入医学伦理学课程的框架设计与实践思考

陈楥帅; 沈思思; 柳华

doi:10.3760/cma.j.cn116021-20250830-02222

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人工智能融入医学伦理学课程的框架设计与实践思考

教学改革 | 更新时间：2026-06-17

- 人工智能融入医学伦理学课程的框架设计与实践思考
- Integrating artificial intelligence into medical ethics curriculum: framework design and practical reflections
- 中华医学教育探索杂志 2026年25卷第5期页码：570-577
- 作者机构：
  
  1.浙江大学医学院附属第二医院人工智能与信息化部，杭州 310009
  2.浙江大学医学院医学教育研究中心，杭州 310058
  3.浙江大学医学院教学办公室，杭州 310058
  4.浙江大学医学院基础医学系，杭州 310058
  5.浙江大学医学院附属第二医院骨科，杭州 310009
- 作者简介：
  
  柳华，Email：liuhua@zju.edu.cn
- 基金信息：
  
  中国高等教育学会2024高等教育科学研究规划重点课题(24BJ0311);中华医学会医学教育分会、全国医学教育发展中心2025年医学教育研究课题(2025B349);浙江省高等教育学会2025高等教育研究重点课题(KT2025001);2025年度浙江大学医学院教育改革项目（本科生教育类）重点项目(jgzd2025001)
- DOI：10.3760/cma.j.cn116021-20250830-02222
  中图分类号： R-052
- 收稿：2025-08-30，
  
  纸质出版：2026-05-20
- 稿件说明：
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陈楥帅, 沈思思, 柳华. 人工智能融入医学伦理学课程的框架设计与实践思考[J]. 中华医学教育探索杂志, 2026, 25(5): 570-577.

Chen Yuanshuai, Shen Sisi, Liu Hua. Integrating artificial intelligence into medical ethics curriculum: framework design and practical reflections[J]. Chinese Journal of Medical Education Research, 2026, 25(5): 570-577.
陈楥帅, 沈思思, 柳华. 人工智能融入医学伦理学课程的框架设计与实践思考[J]. 中华医学教育探索杂志, 2026, 25(5): 570-577. DOI： 10.3760/cma.j.cn116021-20250830-02222.

Chen Yuanshuai, Shen Sisi, Liu Hua. Integrating artificial intelligence into medical ethics curriculum: framework design and practical reflections[J]. Chinese Journal of Medical Education Research, 2026, 25(5): 570-577. DOI： 10.3760/cma.j.cn116021-20250830-02222.

摘要

目的

梳理我国医学伦理学教育的历史演进与现实痛点，比较国际研究进展，提出一个可操作且可评价的人工智能（artificial intelligence，AI）融入医学伦理学课程的框架，并给出实施层面的实践启示。

方法

在综合国内外文献与政策的基础上，归纳出我国医学伦理学教育的4个阶段及主要难点；对国际研究与实践展开对比性梳理；基于“生命伦理四原则”与“算法伦理要点”并行的思路，构建“目标、内容、方法、评价、保障”5维课程框架，并通过学习目标、教学内容、教学活动、评价工具实现课程的映射与闭环对齐。

结果

形成知识、分析、决策3个层级的教学目标；拓展AI伦理核心内容（隐私与合规、可解释性、算法公平、法律责任、伦理责任与专业责任）；采用案例研讨、情景模拟、VR和AR训练、数据偏见识别工作坊及跨学科协作等教学方式；建立与能力相匹配的评价体系[书面案例分析、客观结构化临床考试（objective structured clinical examination，OSCE）和结构化观察、反思Rubric及同行评议]，并强调数据使用的合规性与透明度。

结论

所提出的框架为AI情境下的医学伦理学教育提供了系统化的路径和评价标准，兼顾可操作性与可验证性，可用于指导课程更新和实践落地；后续需通过关于教学成效与学习迁移的实证研究，进一步检验并优化该框架。

Abstract

Objective

To review the historical evolution and current challenges of medical ethics education in China

compare international research progress

propose an operational and evaluable framework for integrating artificial intelligence (AI) into medical ethics curriculum

and provide practical implications for implementation.

Methods

Based on a synthesis of domestic and international literature and policies

we identified four developmental stages and key difficulties in China's medical ethics education. International research and practices were comparatively reviewed. Grounded in the parallel application of the "four principles of biomedical ethics" and "key elements of algorithmic ethics"

a five-dimensional curriculum framework was constructed

including objectives

content

methods

assessment

and support. Curriculum mapping was used to align learning objectives

teaching content

instructional activities

and assessment tools

forming a closed-loop system.

Results

Teaching objectives were established at the three levels of knowledge

analysis

and decision-making. The core AI ethics content was extended

including privacy and compliance

explainability

algorithmic fairness

legal liability

and ethical and professional responsibility. Teaching methods included case discussions

scenario simulations

virtual reality and augmented reality training

data bias identification workshops

and interdisciplinary collaboration. A competency-matched assessment system was established

comprising written case analysis

objective structured clinical examination with structured observation

reflective rubrics

and peer review

with emphasis on the compliance and transparency of data use.

Conclusions

The proposed framework provides a systematic pathway and evaluation standards for medical ethics education in the context of AI. The framework balances operability and verifiability

and can be used to guide curriculum updates and practical implementation. Subsequent empirical research on teaching effectiveness and learning transfer is needed to further test and optimize the framework.

关键词

Keywords

references

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