Reflection on promoting the research capacity of professional master's students in oncology regarding artificial intelligence and big data in the context of the new medical education

Zhou Jianguo; Cai Ying; Hu Wei; He Sisi; Gou Xiaoxia; Li Zhongwen; Liu Xiao; Bai Yuju; Ma Hu

doi:10.3760/cma.j.cn116021-20230320-01590

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Reflection on promoting the research capacity of professional master's students in oncology regarding artificial intelligence and big data in the context of the new medical education

Digital Intelligence MedicalEducation | 更新时间：2025-02-21

- Reflection on promoting the research capacity of professional master's students in oncology regarding artificial intelligence and big data in the context of the new medical education
- Chinese Journal of Medical Education Research Vol. 24, Issue 2, Pages: 160-165(2025)
- 作者机构：
  
  1.遵义医科大学第二附属医院胸部肿瘤科，遵义　563000
  2.遵义医科大学第二附属医院头颈部肿瘤科，遵义　563000
  3.遵义医科大学第二附属医院腹部肿瘤科，遵义　563000
- 作者简介：
  
  Ma Hu, Email: mahuab@163.com
- 基金信息：
  
  Graduate Education and Teaching Reform Project of Zunyi Medical University(ZYK136);National Natural Science Foundation of China(82060475);Cooperation Research Project of Chunhui Program of the Ministry of Education of China(HZKY20220231);Foundation for Basic Research in Guizhou Province(ZK2022-YB632);The Ordinary Undergraduate University Youth Program for Scientific Research in Guizhou Province(黔教技〔2022〕224号)
- DOI：10.3760/cma.j.cn116021-20230320-01590
  CLC： R73
- Received：20 March 2023，
  
  Published：20 February 2025
- 稿件说明：
移动端阅览
周建国, 蔡颍, 胡威, 等. 新医科背景下基于人工智能和大数据提升肿瘤学专硕科研能力的思考[J]. 中华医学教育探索杂志, 2025, 24(2): 160-165.

Zhou Jianguo, Cai Ying, Hu Wei, et al. Reflection on promoting the research capacity of professional master's students in oncology regarding artificial intelligence and big data in the context of the new medical education[J]. Chinese journal of medical education research, 2025, 24(2): 160-165.
周建国, 蔡颍, 胡威, 等. 新医科背景下基于人工智能和大数据提升肿瘤学专硕科研能力的思考[J]. 中华医学教育探索杂志, 2025, 24(2): 160-165. DOI： 10.3760/cma.j.cn116021-20230320-01590.

Zhou Jianguo, Cai Ying, Hu Wei, et al. Reflection on promoting the research capacity of professional master's students in oncology regarding artificial intelligence and big data in the context of the new medical education[J]. Chinese journal of medical education research, 2025, 24(2): 160-165. DOI： 10.3760/cma.j.cn116021-20230320-01590.

摘要

随着全球科技的不断发展和人工智能（artificial intelligence，AI）、大数据时代的到来，肿瘤学专业型研究生的科研能力提升面临新的机遇和挑战。新医科旨在培养能运用多学科综合创新能力解决医学前沿复杂问题的高层次医学人才，特别是要求肿瘤学专业型研究生从临床型向临床科研型复合人才转变。肿瘤学专业型研究生科研产出少，质量良莠不齐，科研素养有待提高。加快推进AI教育及数据库建设，重视肿瘤学专硕科研能力培养，落实推进创新能力和科研思维培养，试行工科高校与医科院校联合培养模式，重视肿瘤学课程建设及导师队伍素质建设，试行多学科培养及“COME”模式，建立多学科合作网络将是促进肿瘤学专业型研究生科研能力培养和提升的重要举措。

Abstract

With the development of science and technology worldwide

the blooming of artificial intelligence (AI) and big data has brought new opportunities and challenges to the promotion of the research capacity of professional master's students in oncology. The construction of the new medical education in China aims to cultivate high-level medical talents with comprehensive multidisciplinary skills and innovative abilities to flexibly solve complex problems at the frontier of medicine. In this context

professional master's students in oncology

who are facing problems such as low scientific research output and uneven quality and needing improving scientific research literacy

have been required to develop into compound talents with both clinical and research prowess. To cultivate and promote the research capacity of professional master's students in oncology

the key steps include accelerating the construction of AI education and databases

highlighting the cultivation of their scientific research capacity

implementing and fostering the cultivation of innovative ability and scientific research thinking

piloting joint cultivation models by engineering universities and medical universities

emphasizing the construction of the curriculum and teacher team for oncology

piloting the multidisciplinary mode and COME mode

and establishing a multidisciplinary cooperation network.

关键词

Keywords

references

吴辉群，蒋葵，董建成，等 . 基于CDIO模式导师制的“新医科”智能医学方向人才培养的探索与实践 [J ] . 中国高等医学教育， 2023 （ 5 ）： 22 - 24 . DOI: 10.3969/j.issn.1002-1701.2023.05.010 http://dx.doi.org/10.3969/j.issn.1002-1701.2023.05.010 .

王智鹏，杨云，钮晓音，等 . 新医科背景下医学专业设置的思考 [J ] . 中华医学教育探索杂志， 2022 ， 21 （ 12 ）： 1601 - 1606 . DOI: 10.3760/cma.j.cn116021-20220830-01091 http://dx.doi.org/10.3760/cma.j.cn116021-20220830-01091 .

刘振强，王启帆，于钦明 . “新医科”背景下我国医学信息教育模式变革与思考 [J ] . 医学信息学杂志， 2022 ， 43 （ 5 ）： 93 - 96 . DOI: 10.3969/j.issn.1673-6036.2022.05.019 http://dx.doi.org/10.3969/j.issn.1673-6036.2022.05.019 .

梁长虹，谢辰仪，吴磊 . 肿瘤诊疗新方向：人工智能与数字化医学图像 [J ] . 兰州大学学报（医学版）， 2023 ， 49 （ 2 ）： 1 - 5 . DOI: 10.13885/j.issn.1000-2812.2023.02.001 http://dx.doi.org/10.13885/j.issn.1000-2812.2023.02.001 .

章颖 . 精准医疗领域的人工智能（AI）治理 [J ] . 中国医药导刊， 2022 ， 24 （ 1 ）： 46 - 49 . DOI: 10.3969/j.issn.1009-0959.2022.01.009 http://dx.doi.org/10.3969/j.issn.1009-0959.2022.01.009 .

赵美荣，王玉红 . 我国城市癌症早诊早治项目研究现状 [J ] . 护理研究， 2023 ， 37 （ 10 ）： 1765 - 1771 . DOI: 10.12102/j.issn.1009-6493.2023.10.014 http://dx.doi.org/10.12102/j.issn.1009-6493.2023.10.014 .

Yanase J , Triantaphyllou E . A systematic survey of computer-aided diagnosis in medicine: past and present developments [J ] . Expert Syst Appl , 2019 , 138 : 112821 . DOI: 10.1016/j.eswa.2019.112821 http://dx.doi.org/10.1016/j.eswa.2019.112821 .

Zhang HY , Zheng JY . The application analysis of medical chatbots and virtual assistant [J ] . Front Soc Sci Technol , 2021 , 3 ( 2 ): 11 - 16 . DOI: 10.25236/fsst.2021.030202 http://dx.doi.org/10.25236/fsst.2021.030202 .

Hayashi N , Maruyama T , Sato Y , et al . Evaluating medical images using deep convolutional neural networks: a simulated CT phantom image study [J ] . Technol Health Care , 2020 , 28 ( 2 ): 113 - 120 . DOI: 10.3233/thc-191718 http://dx.doi.org/10.3233/thc-191718 .

王娜娜，王大为，李大胜，等 . 人工智能辅助诊疗系统在新型冠状病毒肺炎诊疗中的应用初探 [J ] . 中国医疗设备， 2020 ， 35 （ 6 ）： 75 - 79 . DOI: 10.3969/j.issn.1674-1633.2020.06.020 http://dx.doi.org/10.3969/j.issn.1674-1633.2020.06.020 .

Wilson M , Badani K . Competing robotic systems: a preview [J ] . Urol Clin North Am , 2021 , 48 ( 1 ): 147 - 150 . DOI: 10.1016/j.ucl.2020.09.007 http://dx.doi.org/10.1016/j.ucl.2020.09.007 .

Lippross S , Jünemann KP , Osmonov D , et al . Robot assisted spinal surgery: a technical report on the use of DaVinci in orthopaedics [J ] . J Orthop , 2020 , 19 : 50 - 53 . DOI: 10.1016/j.jor.2019.11.045 http://dx.doi.org/10.1016/j.jor.2019.11.045 .

苗红，李男，吴菲菲，等 . 基于机器学习的医学影像人工智能领域技术融合预测 [J ] . 情报杂志， 2022 ， 41 （ 6 ）： 126 - 134 . DOI: 10.3969/j.issn.1002-1965.2022.06.019 http://dx.doi.org/10.3969/j.issn.1002-1965.2022.06.019 .

邹琪华，张宇辰，蔡君，等 . 人工智能在肿瘤领域的应用：科学研究和教学实践中的进展 [J ] . 癌症， 2022 ， 41 （ 2 ）： 49 - 56 .

Luo H , Xu G , Li C , et al . Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study [J ] . Lancet Oncol , 2019 , 20 ( 12 ): 1645 - 1654 . DOI: 10.1016/S1470-2045(19)30637-0 http://dx.doi.org/10.1016/S1470-2045(19)30637-0 .

McKinney SM , Sieniek M , Godbole V , et al . International evaluation of an AI system for breast cancer screening [J ] . Nature , 2020 , 577 ( 7788 ): 89 - 94 . DOI: 10.1038/s41586-019-1799-6 http://dx.doi.org/10.1038/s41586-019-1799-6 .

王可欣，邱建星 . 医学影像非诊断类人工智能（AI）的研究进展 [J ] . 放射学实践， 2023 ， 38 （ 2 ）： 222 - 225 . DOI: 10.13609/j.cnki.1000-0313.2023.02.019 http://dx.doi.org/10.13609/j.cnki.1000-0313.2023.02.019 .

Kather JN , Pearson AT , Halama N , et al . Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer [J ] . Nat Med , 2019 , 25 ( 7 ): 1054 - 1056 . DOI: 10.1038/s41591-019-0462-y http://dx.doi.org/10.1038/s41591-019-0462-y .

Jackson HW , Fischer JR , Zanotelli VRT , et al . The single-cell pathology landscape of breast cancer [J ] . Nature , 2020 , 578 ( 7796 ): 615 - 620 . DOI: 10.1038/s41586-019-1876-x http://dx.doi.org/10.1038/s41586-019-1876-x .

Campanella G , Hanna MG , Geneslaw L , et al . Clinical-grade computational pathology using weakly supervised deep learning on whole slide images [J ] . Nat Med , 2019 , 25 ( 8 ): 1301 - 1309 . DOI: 10.1038/s41591-019-0508-1 http://dx.doi.org/10.1038/s41591-019-0508-1 .

Fu Y , Jung AW , Torne RV , et al . Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis [J ] . Nat Cancer , 2020 , 1 ( 8 ): 800 - 810 . DOI: 10.1038/s43018-020-0085-8 http://dx.doi.org/10.1038/s43018-020-0085-8 .

Courtiol P , Maussion C , Moarii M , et al . Deep learning-based classification of mesothelioma improves prediction of patient outcome [J ] . Nat Med , 2019 , 25 ( 10 ): 1519 - 1525 . DOI: 10.1038/s41591-019-0583-3 http://dx.doi.org/10.1038/s41591-019-0583-3 .

平轶芳，闫红，卞修武 . 人工智能病理在肿瘤精准医疗时代的应用与挑战 [J ] . 生命科学， 2022 ， 34 （ 8 ）： 929 - 940 . DOI: 10.13376/j.cbls/2022103 http://dx.doi.org/10.13376/j.cbls/2022103 .

王卓，吴祺，王花丽，等 . 人工智能技术在乳腺癌病理诊断中的应用进展 [J ] . 现代肿瘤医学， 2023 ， 31 （ 1 ）： 174 - 177 . DOI: 10.3969/j.issn.1672-4992.2023.01.033 http://dx.doi.org/10.3969/j.issn.1672-4992.2023.01.033 .

Qaiser T , Mukherjee A , Pb CR , et al . HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues [J ] . Histopathology , 2018 , 72 ( 2 ): 227 - 238 . DOI: 10.1111/his.13333 http://dx.doi.org/10.1111/his.13333 .

Feng M , Deng Y , Yang L , et al . Automated quantitative analysis of Ki-67 staining and HE images recognition and registration based on whole tissue sections in breast carcinoma [J ] . Diagn Pathol , 2020 , 15 ( 1 ): 65 . DOI: 10.1186/s13000-020-00957-5 http://dx.doi.org/10.1186/s13000-020-00957-5 .

吴双，周育夫 . 人工智能在肿瘤放射治疗中的应用 [J ] . 中国医疗设备， 2020 ， 35 （ 8 ）： 161 - 166 .

张婕 . 人工智能在医学中的应用现状与展望 [J ] . 数字通信世界， 2022 （ 12 ）： 172 - 174 . DOI: 10.3969/j.issn.1672-7274.2022.12.057 http://dx.doi.org/10.3969/j.issn.1672-7274.2022.12.057 .

刘国慧，贺云龙，兰天，等 . 创新型肿瘤放射治疗学研究生培养模式的探索 [J ] . 中国继续医学教育， 2020 ， 12 （ 33 ）： 55 - 58 . DOI: 10.3969/j.issn.1674-9308.2020.33.017 http://dx.doi.org/10.3969/j.issn.1674-9308.2020.33.017 .

薛鹏，陈栋晖，朱莉菲，等 . 住院医师规范化培训肿瘤学教学模式探讨 [J ] . 继续医学教育， 2021 ， 35 （ 1 ）： 54 - 55 . DOI: 10.3969/j.issn.1004-6763.2021.01.031 http://dx.doi.org/10.3969/j.issn.1004-6763.2021.01.031 .

徐静，李晓瑜，方玉凤，等 . 大数据时代临床医学硕士专业学位研究生临床科研能力培养探索 [J ] . 中国高等医学教育， 2022 （ 6 ）： 128 - 130 . DOI: 10.3969/j.issn.1002-1701.2022.06.065 http://dx.doi.org/10.3969/j.issn.1002-1701.2022.06.065 .

Brouillette M . AI added to the curriculum for doctors-to-be [J ] . Nat Med , 2019 , 25 ( 12 ): 1808 - 1809 . DOI: 10.1038/s41591-019-0648-3 http://dx.doi.org/10.1038/s41591-019-0648-3 .

李鸿浩，陈波，李建平，等 . 医学教育中人工智能应用的现状、问题与对策 [J ] . 中国循证医学杂志， 2020 ， 20 （ 9 ）： 1092 - 1097 . DOI: 10.7507/1672-2531.201910092 http://dx.doi.org/10.7507/1672-2531.201910092 .

Rimmer A . Technology will improve doctors' relationships with patients, says Topol review [J ] . BMJ , 2019 , 364 : l661 . DOI: 10.1136/bmj.l661 http://dx.doi.org/10.1136/bmj.l661 .

李伟锋，何峰，杜育任，等 . 智能医学赋能未来 [J ] . 交通医学， 2019 ， 33 （ 6 ）： 551 - 554 . DOI: 10.19767/j.cnki.32-1412.2019.06.004 http://dx.doi.org/10.19767/j.cnki.32-1412.2019.06.004 .

梅文瀚，朱坚轶，苏懿，等 . “4+4” 临床医学人才培养美国模式与交医模式的比较及思考 [J ] . 中华医学教育探索杂志， 2022 ， 21 （ 12 ）： 1606 - 1611 . DOI: 10.3760/cma.j.cn116021-20221003-01092 http://dx.doi.org/10.3760/cma.j.cn116021-20221003-01092 .

Chan KS , Zary N . Applications and challenges of implementing artificial intelligence in medical education: integrative review [J ] . JMIR Med Educ , 2019 , 5 ( 1 ): e13930 . DOI: 10.2196/13930 http://dx.doi.org/10.2196/13930 .

Kolachalama VB , Garg PS . Machine learning and medical education [J ] . NPJ Digit Med , 2018 , 1 : 54 . DOI: 10.1038/s41746-018-0061-1 http://dx.doi.org/10.1038/s41746-018-0061-1 .

陈犹白，许育健，邢家华，等 . 解放军总医院整形修复科硕士研究生科研能力的 “COME” 培养模式 [J ] . 中华整形外科杂志， 2023 ， 39 （ 1 ）： 90 - 94 . DOI: 10.3760/cma.j.cn114453-20220331-00091 http://dx.doi.org/10.3760/cma.j.cn114453-20220331-00091 .

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