Artificial Intelligence in Medical Education: Current Research and Applications

Min Li; Helin Song; Menghan Tian; Yijia Ren; Sixian Li

doi:10.47297/wspiedWSP2516-250032.20250909

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Artificial Intelligence in Medical Education: Current Research and Applications

Volume 9, Issue 9

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Artificial Intelligence in Medical Education: Current Research and Applications
Artificial Intelligence in Medical Education: Current Research and Applications
1.The Second Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, 750004, China
2.School of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, 750004, China
3.The First Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, 750004, China
4.The Third Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, 750004, China
Vol. 9, Issue 9, Pages: 144-147(2025)
Published：30 September 2025
DOI：10.47297/wspiedWSP2516-250032.20250909
Accepted：

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Li Min,Song Helin,Tian Menghan,et al.Artificial Intelligence in Medical Education: Current Research and Applications[J].Journal of International Education and Development,2025,09(09):144-147.
DOI：

Li Min,Song Helin,Tian Menghan,et al.Artificial Intelligence in Medical Education: Current Research and Applications[J].Journal of International Education and Development,2025,09(09):144-147. DOI： 10.47297/wspiedWSP2516-250032.20250909.

摘要

Abstract

As medical education undergoes continuous transformation

the integration of artificial intelligence (AI) into teaching practices has attracted growing attention. AI has demonstrated considerable potential in virtual simulation

personalized learning support

clinical reasoning training

humanistic education

and interdisciplinary teaching. These applications have been shown to enhance instructional quality

optimize the learning experience

and foster educational innovation. Evidence indicates that AI not only delivers real-time feedback and targeted training but also facilitates the development of students' cognitive abilities and practical skills in complex clinical scenarios. Nevertheless

its widespread adoption faces persistent challenges

including limited acceptance among faculty and students

unequal distribution of resources

inadequate assessment frameworks

and lagging institutional support. Future progress should emphasize large-scale

multicenter empirical studies

promote deeper integration of technology with curricula

strengthen resource sharing and interdisciplinary talent cultivation

and establish unified standards and quality assurance systems. Such measures are essential for achieving the sustainable implementation of AI in medical education and maximizing its educational value.

关键词

Keywords

references

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