医学教育管理 ›› 2026, Vol. 12 ›› Issue (4): 496-502.doi: 10.3969/j.issn.2096-045X.2026.04.010

• 人才培养 • 上一篇    下一篇

生成式人工智能赋能医学生培养的现实挑战与实践路径

  

  1. 1.首都医科大学马克思主义学院,北京 100069;2.首都医科大学基础医学院,北京 100069;3.承德县中医院手术室,承德 067400
  • 收稿日期:2025-06-04 修回日期:2026-03-19 接受日期:2026-04-13 出版日期:2026-08-20 发布日期:2026-09-01

Realistic challenges and practical pathways of generative artificial intelligence–empowered medical student training

  1. 1. School of Marxism, Capital Medical University, Beijing 100069, China; 2. School of Basic Medicine, Capital Medical University, Beijing 100069, China; 3. Operating Room, Chengde County Hospital of Traditional Chinese Medicine, Chengde 067400, China
  • Received:2025-06-04 Revised:2026-03-19 Accepted:2026-04-13 Online:2026-08-20 Published:2026-09-01

摘要: 应对人工智能技术在医疗卫生领域引起的诊疗方式变革,以及满足健康中国建设对数智化医学人才的现实需求。本文围绕生成式人工智能(generative artificial intelligence,GenAI)赋能医学生培养相关议题,对医学生培养面临的现实挑战及实践路径展开研究。研究发现GenAI赋能医学生培养仍面临引入医学院校存在障碍、GenAI高阶能力开发应用不够充分以及GenAI在使用中存在误导风险三大核心问题。基于此,本文提出充分发挥GenAI优势,辅助教学支持;深化GenAI应用,赋能科研与临床培养;规范GenAI使用,防范技术误导风险3条创新路径,以期为数智化临床环境下复合型医学人才的培养提供实践参考。

Abstract:

In response to the transformation of diagnosis and treatment models brought about by artificial intelligence technologies in the healthcare sector, and to meet the practical demand of the Healthy China initiative for medical talents adapted to digital and intelligent development, this paper focuses on issues related to generative artificial intelligence (GenAI)-empowered medical student training and conducts research on the practical challenges and implementation pathways faced by medical student education. The findings suggest that GenAI-empowered medical student training still faces three major challenges: barriers to its introduction into medical schools, insufficient development and application of the advanced capabilities of GenAI, and the risk of misleading information in its use. Accordingly, this paper proposes three innovative pathways: fully leveraging GenAI's advantages to support teaching assistance; deepening the application of GenAI to empower research training and clinical education; and regulating GenAI usage to prevent technology-induced misinformation risks. This study aims to provide practical reference for the cultivation of interdisciplinary medical talents in digitally and intelligently evolving clinical environments.