The data-intensive research paradigm: challenges and responses in clinical professional graduate education
With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. This new paradigm presents unprecedented challenges for graduate ed...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1461863/full |
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| author | Chunhong Yang Yijing Chen Changshun Qian Fangmin Shi Fangmin Shi You Guo You Guo You Guo You Guo You Guo |
| author_facet | Chunhong Yang Yijing Chen Changshun Qian Fangmin Shi Fangmin Shi You Guo You Guo You Guo You Guo You Guo |
| author_sort | Chunhong Yang |
| collection | DOAJ |
| description | With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. This new paradigm presents unprecedented challenges for graduate education in clinical professions, encompassing multidisciplinary integration needs, high-quality faculty shortages, learning method transformations, assessment system updates, and ethical concerns. Future healthcare professionals will need not only to possess traditional medical knowledge and clinical skills, but also to master interdisciplinary skills such as data analysis, programming, and statistics. In response, this paper proposes a series of countermeasures, including curriculum reconstruction, faculty development, developing and sharing resources, updating the evaluation and assessment system, and strengthening ethics education. These initiatives aim to help clinical graduate education better adapt to this new paradigm, ultimately cultivating interdisciplinary talents in medical-computer integration. |
| format | Article |
| id | doaj-art-8af2078fafb049728a2a36c259698e1f |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-8af2078fafb049728a2a36c259698e1f2025-02-07T06:49:51ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-02-011210.3389/fmed.2025.14618631461863The data-intensive research paradigm: challenges and responses in clinical professional graduate educationChunhong Yang0Yijing Chen1Changshun Qian2Fangmin Shi3Fangmin Shi4You Guo5You Guo6You Guo7You Guo8You Guo9Academic Affairs Office, First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaSchool of Public Health and Health Management, Gannan Medical University, Ganzhou, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaSchool of Public Health and Health Management, Gannan Medical University, Ganzhou, ChinaMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaAcademic Affairs Office, First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaSchool of Public Health and Health Management, Gannan Medical University, Ganzhou, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, ChinaMedical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaGanzhou Key Laboratory of Medical Big Data, Ganzhou, ChinaWith the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. This new paradigm presents unprecedented challenges for graduate education in clinical professions, encompassing multidisciplinary integration needs, high-quality faculty shortages, learning method transformations, assessment system updates, and ethical concerns. Future healthcare professionals will need not only to possess traditional medical knowledge and clinical skills, but also to master interdisciplinary skills such as data analysis, programming, and statistics. In response, this paper proposes a series of countermeasures, including curriculum reconstruction, faculty development, developing and sharing resources, updating the evaluation and assessment system, and strengthening ethics education. These initiatives aim to help clinical graduate education better adapt to this new paradigm, ultimately cultivating interdisciplinary talents in medical-computer integration.https://www.frontiersin.org/articles/10.3389/fmed.2025.1461863/fulldata-intensive researchclinical medicinegraduate educationchallengesresponses |
| spellingShingle | Chunhong Yang Yijing Chen Changshun Qian Fangmin Shi Fangmin Shi You Guo You Guo You Guo You Guo You Guo The data-intensive research paradigm: challenges and responses in clinical professional graduate education Frontiers in Medicine data-intensive research clinical medicine graduate education challenges responses |
| title | The data-intensive research paradigm: challenges and responses in clinical professional graduate education |
| title_full | The data-intensive research paradigm: challenges and responses in clinical professional graduate education |
| title_fullStr | The data-intensive research paradigm: challenges and responses in clinical professional graduate education |
| title_full_unstemmed | The data-intensive research paradigm: challenges and responses in clinical professional graduate education |
| title_short | The data-intensive research paradigm: challenges and responses in clinical professional graduate education |
| title_sort | data intensive research paradigm challenges and responses in clinical professional graduate education |
| topic | data-intensive research clinical medicine graduate education challenges responses |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1461863/full |
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