Theoretical framework and practical research of the "Cloud-Track-AI-Collaborative" training model for AI-empowered medical professionals

Fan Ruitao; Lu Jiayu; Zhang Xuxu; Li Jiahe; Zhang Jipeng; Li Wei; Lu Qiang

doi:10.3760/cma.j.cn116021-20260506-02397

您当前的位置：

首页 >

文章列表页 >

Theoretical framework and practical research of the "Cloud-Track-AI-Collaborative" training model for AI-empowered medical professionals

更新时间：2026-11-02

- Theoretical framework and practical research of the "Cloud-Track-AI-Collaborative" training model for AI-empowered medical professionals
- Chinese Journal of Medical Education Research Pages: 1-8(2026)
- 作者机构：
  
  空军军医大学唐都医院胸腔外科，西安 710038
- 作者简介：
  
  Lu Qiang, Email: luqiang@fmmu.edu.cn
- 基金信息：
  
  Shaanxi Province Key Core Technology Research Project(2024SF-GJHX-27);Discipline Boost Plan of Tangdu Hospital, Air Force Medical University(2024JSYX005);Air Force Medical University Teaching Achievement Cultivation Project(XCG2024PY13);Shaanxi Province Undergraduate and Higher Continuing Education Teaching Reform Research Project(23JZ008)
- DOI：10.3760/cma.j.cn116021-20260506-02397
  CLC： R-05;R-4
- Received：06 May 2026，
  
  Online First：02 November 2026，
- 稿件说明：
移动端阅览
Fan Ruitao, Lu Jiayu, Zhang Xuxu, et al. Theoretical framework and practical research of the "Cloud-Track-AI-Collaborative" training model for AI-empowered medical professionals[J/OL]. Chinese Journal of Medical Education Research, 2026, 1-8.
DOI：

Fan Ruitao, Lu Jiayu, Zhang Xuxu, et al. Theoretical framework and practical research of the "Cloud-Track-AI-Collaborative" training model for AI-empowered medical professionals[J/OL]. Chinese Journal of Medical Education Research, 2026, 1-8. DOI： 10.3760/cma.j.cn116021-20260506-02397.

摘要

目的

适应“新医科”建设对医学人才数字化、智能化培养的新要求，构建并验证“云轨智协”培养模式的可行性与有效性。

方法

融合教育云平台、PBL+TBL双轨教学与人工智能辅助三大要素，构建“云轨智协”一体化培养模式。选取2025年1月至12月在空军军医大学唐都医院胸外科轮转的120名临床医学专业学位硕士研究生为研究对象，这些学生分为12个批次，随机分配至试验组（“云轨智协”模式，

=60）和对照组（单纯双轨教学，n=60）。轮转结束后，比较两组学生的学习效果和评价。采用SPSS 26.0进行统计分析。

结果

试验组理论[（44.18±3.12）分 vs. （40.13±3.48）分

]

、操作技能[（43.82±3.01）分 vs. （39.52±3.42）分

]

及专科能力[（42

.87±3.24）分 vs. （38.73±3.58）分

]

考核成绩均高于对照组（均

0.001）；专业实践能力各维度[如临床思维逻辑（22.93±1.11）分 vs. （20.85±1.16）分

]

、论文答辩各维度及教学评价各条目评分亦均高于对照组（均

0.001）。

结论

“云轨智协”培养模式能有效提升医学专业学位硕士研究生的综合能力与学习满意度，为AI赋能型医学人才培养提供了可行的系统化路径。

Abstract

Objective

To meet the new requirements of digital and intelligent medical talent development under the "New Medical Science" initiative

this study aimed to establish the "Cloud-Track-AI-Collaborative" training model and validate its feasibility and effectiveness.

Methods

Integrating three core elements

namely an educational cloud platform

dual-track teaching (problem-based learning plus team-based learning)

and artificial intelligence assistance

we developed an integrated "Cloud-Track-AI-Collaborative" training model. A total of 120 professional master's degree students in clinical medicine rotating through the Department of Thoracic Surgery at Tangdu Hospital

Air Force Medical University

from January to December 2025 were selected as participants. These students were divided into 12 batches and randomly assigned to either the experimental group ("Cloud-Track-AI-Collaborative" model

=60) or the control group (dual-track teaching only

=60). After the rotation period

learning outcomes and evaluations were compared between the two groups. SPSS 26.0 was used for statistical analysis.

Results

The experimental group scored higher than the control group in theoretical knowledge [(44.18±3.12) vs. (40.13±3.48) points

]

procedural skills [(43.82±3.01) vs. (39.52±3.42) points

]

specialty competency [(42.87±3.24) vs. (38.73±3.58) points

]

all dimensions of professional practice competency [e.g.

clinical reasoning (22.93±1.11) vs. (20.85±1.16) points

]

all dimensions of thesis defense

and all items of teaching evaluation (all

0.001

Conclusions

The "Cloud-Track-AI-Collaborative" training model effectively enhances the comprehensive competencies and learning satisfaction of professional master's degree students in medicine

providing a feasible and systematic pathway for the training of AI-empowered medical professionals.

关键词

Keywords

references

许燕萍，黄烨清 . PBL教学模式在医学生教育中的有效性研究：基于随机对照试验的meta分析 [J ] . 中国高等医学教育 , 2024 （ 4 ）： 23 - 25 . DOI: 10.3969/j.issn.1002-1701.2024.04.009 http://dx.doi.org/10.3969/j.issn.1002-1701.2024.04.009 .

王启帆，刘雨 . 数字化赋能医学教育:应用、困境与路径 [J ] . 医学与哲学， 2024 ， 45 （ 10 ）： 65 - 68 . DOI: 10.12014/j.issn.1002-0772.2024.10.14 http://dx.doi.org/10.12014/j.issn.1002-0772.2024.10.14 .

黄智若，沈晓沛，陈湖星，等 . “医学+人工智能”复合型创新人才培养模式探析 [J ] . 中国医学教育技术， 2024 ， 38 （ 3 ）： 271 - 275 .

赵晋波，陈召，李小飞，等 . PBL教学法与批判性思维在医学本科生胸外科临床教学中的应用研究 [J ] . 现代生物医学进展， 2014 ， 14 （ 32 ）： 6355 - 6359 .

Gera M , Rathod U , Karra-Aly A , et al . Team based learning vs problem based learning in medical education: a systematic review [J ] . Eastern J Med Sci , 2023 : 1 - 6 . DOI: 10.32677/ejms.v8i1.3795 http://dx.doi.org/10.32677/ejms.v8i1.3795 .

邹鑫，李小飞，周勇安，等 . PBL和TBL的双轨教学法在胸外科专业学位研究生教学中的应用 [J ] . 现代生物医学进展， 2016 ， 16 （ 7 ）： 1345 - 1349 .

朱文娟，王明礼，徐测梁，等 . PBL联合TBL应用于急诊医学临床教学观察 [J ] . 新疆中医药， 2023 ， 41 （ 6 ）： 60 - 62 .

张卫强，赵京，韩聚强，等 . CBL结合PBL及LBL教学方法在胸外科临床实习中的应用 [J ] . 现代生物医学进展， 2013 ， 13 （ 24 ）： 4777 - 4780 . DOI: 10.13241/j.cnki.pmb.2013.24.008 http://dx.doi.org/10.13241/j.cnki.pmb.2013.24.008 .

章泽昂，邬家炜 . 基于云计算的教育信息化平台的研究 [J ] . 中国远程教育， 2010 ，（ 6 ）： 66 - 69+80 . DOI: 10.13541/j.cnki.chinade.2010.06.015 http://dx.doi.org/10.13541/j.cnki.chinade.2010.06.015 .

魏嵬，罗高萌，刘润铭，等 . 人工智能辅助教学在医学教育中应用效果的Meta分析 [J ] . 医学新知， 2025 ， 35 （ 10 ）： 1242 - 1248 .

韩淏轩，吕枫枫，王强，等 . 人工智能技术赋能医学人才培养的应用与探究：以首都医科大学为例 [J ] . 中国医学教育技术， 2024 ， 38 （ 3 ）： 261 - 265， 270 .

Liu P , Zhang J , Chen S , et al . Human-AI teaming in healthcare: 1 + 1 > 2? [J ] . npj Artif Intell , 2025 , 1 : 47 . DOI: 10.1038/s44387-025-00052-4 http://dx.doi.org/10.1038/s44387-025-00052-4 .

卢强，王东光，陈慧芸，等 . “教育云+双轨” 教学在胸外科继续医学教育中的应用研究 [J ] . 中华医学教育探索杂志， 2021 ， 20 （ 9 ）： 982 - 986 . DOI: 10.3760/cma.j.cn116021-20200402-00611 http://dx.doi.org/10.3760/cma.j.cn116021-20200402-00611 .

李泽睿，崔维顶，吴远帆 . DeepSeek赋能医学智能化教学新模式的探索与挑战 [J ] . 江苏科技信息， 2025 ， 42 （ 11 ）： 95 - 99 . DOI: 10.3969/j.issn.1004-7530.2025.11.020 http://dx.doi.org/10.3969/j.issn.1004-7530.2025.11.020 .

Sohrabi Z , Kavarizadeh F , Karimi E , et al . Benefits and challenges of using learning analytics in medical education: a scoping review [J ] . Interdisciplinary Journal of Virtual Learning in Medical Sciences , 2025 , 16 ( 4 ): 343 - 361 . DOI: 10.30476/ijvlms.2025.107744.1345 http://dx.doi.org/10.30476/ijvlms.2025.107744.1345 .

Wang Y , Wang P , Zhu J , et al . Construction and application of clinical nursing intern teaching system from the perspective of educational ecology: a quasi-experiment study [J ] . Medicine , 2026 , 105 ( 4 ): e45593 . DOI: 10.1097/MD.0000000000045593 http://dx.doi.org/10.1097/MD.0000000000045593 .[PubMed ]

Tran M , Balasooriya C , Semmler C , et al . Generative artificial intelligence: the 'more knowledgeable other' in a social constructivist framework of medical education [J ] . npj Digit Med , 2025 , 8 : 430 . DOI: 10.1038/s41746-025-01823-8 http://dx.doi.org/10.1038/s41746-025-01823-8 .[LinkOut ]

Lee J , Wu AS , Li D , et al . Artificial intelligence in undergraduate medical education: a scoping review [J ] . Acad Med , 2021 , 96 ( 11S ): S62 - S70 . DOI: 10.1097/ACM.0000000000004291 http://dx.doi.org/10.1097/ACM.0000000000004291 .[PubMed ]

Norcini J , Anderson B , Bollela V , et al . Criteria for good assessment: Consensus statement and recommendations from the Ottawa 2010 Conference [J ] . Med Teach , 2011 , 33 ( 3 ): 206 - 214 . DOI: 10.3109/0142159X.2011.551559 http://dx.doi.org/10.3109/0142159X.2011.551559 .[LinkOut ]

Sarwar T , Antonio José Jimeno Yepes , Cavedon L . Assessing the impact of the quality of textual data on feature representation and machine learning models: quantitative study using large language models [J ] . Journal of Medical Internet Research , 2025 , 27 : e73325 . DOI: 10.2196/73325 http://dx.doi.org/10.2196/73325 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Artificial intelligence-enhanced teaching of neurological physical examination: an exploratory study on its effectiveness

Application effectiveness of an artificial intelligence-assisted handwashing robot in surgical hand antisepsis training for medical students

Development and application of an artificial intelligence-assisted learning platform for the entire process of medical education

A survey on the current use and ethical awareness of artificial intelligence among medical trainees

Analysis of common issues regarding clinical teaching research and papers in medical education in China and reflections on quality improvement strategies

Related Author

Zhi Nan

Yao Xiaoying

Li Yi

Pan Yuanmei

Wang Gang

Liu Huayue

Yan Zihan

Fu Chong

Related Institution

Department of Neurology, Renji Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai Joint School of Medicine, Shanghai Jiao Tong University School of Medicine

Department of Anesthesiology, The First Affiliated Hospital of Soochow University

Suzhou Medical College of Soochow University

Clinical Training Office, The First Affiliated Hospital of Soochow University

⁰