医学教育管理 ›› 2025, Vol. 11 ›› Issue (5): 523-528.doi: 10.3969/j.issn.2096-045X.2025.05.005

• 教育教学 • 上一篇    下一篇

疾病表型-分子机制双轴融合的智慧教学体系构建

郑君芳,  陈子钰,  王雯*   

  1. 首都医科大学基础医学院,北京 100069
  • 收稿日期:2025-02-26 修回日期:2025-05-16 出版日期:2025-10-20 发布日期:2025-11-07
  • 通讯作者: 王雯 E-mail:wangwen@ccmu.edu.cn
  • 基金资助:
    北京市高等教育学会2024年立项课题项目(MS2024197)

Construction of intelligent teaching system with biaxial fusion of disease phenotype and molecular mechanism

Zheng Junfang, Chen Ziyu, Wang Wen*   

  1. School of Basic Medicine, Capital Medical University, Beijing 100069, China
  • Received:2025-02-26 Revised:2025-05-16 Online:2025-10-20 Published:2025-11-07

摘要: “新医科”教育改革背景下,跨学科临床思维能力培养成为卓越医学人才培养的关键维度。当前医学整合教学研究多关注课程模块的机械组合,对如何通过知识重构实现学科深层关联、如何运用智能技术促进临床认知转化等核心问题的探索尚有不足。本研究以系统医学理论为指导框架,从知识图谱构建、案例驱动学习与实验验证链设计三个维度,构建 “疾病表型-分子机制” 双轴联动的病理生理学与生物化学融合教学模式。通过建立“分子-功能-系统”三级整合模型,开发临床案例与虚拟仿真实验的动态匹配机制,并结合典型教学案例,阐释如何借助人工智能(artificial intelligence,AI)实现学生认知水平与案例复杂度的精准适配,随后设计基于实验验证链的认知训练路径,最终形成多模态交互式学习体系。本研究为揭示临床思维培养的认知转化机制提供了新视角,并为推进医学整合课程改革、提升医学生系统医学素养提供实践范式。

关键词: 跨学科整合, 分子机制, 疾病表型, AI 辅助教学, 智慧医学教育

Abstract: Under the background of "new medical sciences" education reform, the cultivation of interdisciplinary clinical thinking ability has become a key dimension of the cultivation of outstanding medical talents. The current research on integrated medical teaching focuses on the mechanical combination of course modules, but the core issues how to achieve in-depth interdisciplinary connections through knowledge reconstruction and how to use intelligent technology to promote clinical cognitive transformation are not explored enough. This study, guided by the theory of systems medicine, constructs an integrated teaching model of pathophysiology and biochemistry with the dual-axis linkage of "disease phenotype - molecular mechanism" from three dimensions: knowledge graph construction, case-driven learning, and experimental verification chain design. By establishing a three-level integration model of "molecule - function - system", a dynamic matching mechanism between clinical cases and virtual simulation experiments was developed. Combined with typical teaching cases, this study explains how to use artificial intelligence (AI) to achieve accurate matching between students' cognitive levels and case complexity, then designs a cognitive training path based on the experimental verification chain, and finally forms a multimodal interactive learning system. This study provides a new perspective to reveal the cognitive transformation mechanism of clinical thinking training, and provides a practical model for promoting the reform of medical integration curriculum and improving the systematic medical literacy of medical students.

Key words: interdisciplinary integration, molecular mechanism, disease phenotype, AI-assisted teaching, intelligent medical education

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