Differences in the application of artificial intelligence tools in domestic and overseas scenarios among medical staff and their implications

Hu Xin; Chen Shaohua; Shi Beiyi; Deng Tianyi

doi:10.3760/cma.j.cn116021-20260330-02320

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Differences in the application of artificial intelligence tools in domestic and overseas scenarios among medical staff and their implications

Digital Intelligence MedicalEducation | 更新时间：2026-07-27

- Differences in the application of artificial intelligence tools in domestic and overseas scenarios among medical staff and their implications
- Chinese Journal of Medical Education Research Vol. 25, Issue 7, Pages: 793-799(2026)
- 作者机构：
  
  1.浙江大学医学院附属第一医院国际交流部，杭州 310003
  2.浙江大学医学院附属第一医院教学部，杭州 310003
- 作者简介：
  
  Hu Xin, Email: huxin1030@zju.edu.cn
- 基金信息：
  
  Medical Education Research Project of the Medical Education Branch of the Chinese Medical Association(2025B342);General Scientific Research Project of the Department of Education of Zhejiang Province(Y202557769);Research Project Commissioned by Zhejiang Provincial Hospital Association
- DOI：10.3760/cma.j.cn116021-20260330-02320
  CLC： R-0;R-4;R-05
- Received：30 March 2026，
  
  Online First：01 July 2026，
  
  Published：20 July 2026
- 稿件说明：
移动端阅览
胡欣, 陈韶华, 史贝裔, 等. 医务人员国内外场景下AI工具应用差异及启示[J]. 中华医学教育探索杂志, 2026, 25(7): 793-799.

Hu Xin, Chen Shaohua, Shi Beiyi, et al. Differences in the application of artificial intelligence tools in domestic and overseas scenarios among medical staff and their implications[J]. Chinese Journal of Medical Education Research, 2026, 25(7): 793-799.
胡欣, 陈韶华, 史贝裔, 等. 医务人员国内外场景下AI工具应用差异及启示[J]. 中华医学教育探索杂志, 2026, 25(7): 793-799. DOI： 10.3760/cma.j.cn116021-20260330-02320.

Hu Xin, Chen Shaohua, Shi Beiyi, et al. Differences in the application of artificial intelligence tools in domestic and overseas scenarios among medical staff and their implications[J]. Chinese Journal of Medical Education Research, 2026, 25(7): 793-799. DOI： 10.3760/cma.j.cn116021-20260330-02320.

摘要

目的

探讨医务人员在国内外学习与实践场景下人工智能（artificial intelligence，AI）工具的应用特征、需求差异与场景衔接问题，为完善国际化医学人才培养与继续医学教育模式提供实证依据。

方法

以浙江大学医学院附属第一医院205名具有出国学习与实践经历的医务人员为研究对象。采用电子问卷开展调查，对比国内外场景中AI工具使用频率、满意度、应用场景与价值认同度。运用SPSS 26.0进行统计学分析，对开放式问题采用主题归纳法进行质性梳理。

结果

医务人员在国内场景的AI使用频率显著高于出国场景（3.04±0.92 vs. 2.63±1.10，

0.001）；2种场景满意度均偏低且差异无统计学意义（1.90±1.08 vs. 1.91±1.09，

0.05）；价值认同度随年龄增长显著升高（

0.05），总体呈现认同度较高、满意度偏低的鲜明反差。应用场景呈明显分化：国内以科研数据处理为主，国外以跨文化沟通为主，AI工具难以在双场景间平稳衔接与双向迁移。数据隐私安全限制、场景贴合度不足、专用教学资源缺乏是主要制约因素。

结论

医务人员在国内外场景中的AI应用存在显著的需求分化与衔接壁垒，其差异不仅源于使用条件的不同，更反映了医疗制度、数据法规、文化习惯与教育模式的深层区别。未来应构建全周期、国内外衔接的AI学习支持体系，强化医务人员AI素养培育，提升继续医学教育质量，推动医学人才国际化培养与医院高质量发展。

Abstract

Objective

To investigate the application characteristics

demand differences

and scenario transition of artificial intelligence (AI) tools used by medical staff in domestic and overseas learning and practice scenarios

and provide empirical evidence for improving international medical talent training and continuing medical education models.

Methods

An electronic questionnaire survey was conducted among 205 medical staff with overseas learning and practice experience from The First Affiliated Hospital

Zhejiang University School of Medicine. We compared the usage frequency

satisfaction

application scenarios

and value recognition of AI tools between domestic and overseas scenarios. Statistical analysis was performed using SPSS 26.0. Open-ended responses were subjected to qualitative thematic analysis.

Results

The usage frequency of AI tools was significantly higher in domestic scenarios than in overseas scenarios (3.04±0.92 vs. 2.63±1.10

0.001). Satisfaction was low in both scenarios with no significant difference (1.90±1.08 vs. 1.91±1.09

0.05). Value recognition increased significantly with age (

0.05)

showing a clear contrast of high recognition but low satisfaction. Application scenarios showed clear divergence. Domestic use focused on research data processing

while overseas use centered on cross-cultural communication. Smooth transition and bidirectional transfer of AI tools between the two scenarios were difficult. Major constraints included data privacy restrictions

insufficient scenario fit

and a lack of dedicated educational resources.

Conclusions

Significant demand divergences and transition barriers exist in AI application among medical staff in domestic and overseas scenarios. These differences not only arise from usage conditions but also reflect deep-seated distinctions in medical systems

data regulations

cultural habits

and educational models. A full-cycle

cross-border AI learning support system should be established in the future to strengthen the AI literacy of medical staff

improve the quality of continuing medical education

and promote the international training of medical talent and the high-quality development of hospitals.

关键词

Keywords

references

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