机器学习在医学专业教育教学评价中应用的范围综述
A Review of the Application Scope of Machine Learning in Medical Education and Teaching Evaluation

目的：对在医学专业教育教学评价体系中应用机器学习的相关研究进行范围综述，为医学专业教育教学评估体系发展提供新思路。方法：依据范围综述的方法框架，确立研究问题，系统检索中国知网、万方、维普、PubMed、Web of Science 5个中英文库，检索时间为建库至2022年3月15日。筛选符合纳入标准的文献，并对文献进行分析讨论。结果：共检索文献301篇，纳入文献15篇。总结显示，机器学习主要应用在学生能力评价、课程反馈信息处理、预测和识别高危学生等方面。应用机器学习形成的教育教学评价模型评价效果较好，其在提高评价效率、节约评价成本、构建科学的评价指标等方面具有较大优势。结论：目前将机器学习应用于医学专业教育教学评价的研究较少，现有研究验证了机器学习在医学教育教学评价中应用的可行性，但其准确性、泛化性和有效性等方面仍待进一步完善。
Objective: To review the research on the application of machine learning in the evaluation system of medical professional education and teaching, and to provide new ideas for the development of medical professional education and teaching system. Methods: According to the method framework of scope review, the research questions were identified and the five Chinese and English databases of CNKI, Wanfang, VIP, PubMed and Web of Science were searched systematically from the establishment of the database to March 15, 2022. The literatures that met the inclusion criteria were screened and analyzed. Results: A total of 301 papers were retrieved, 15 of which were included according to the inclusion criteria. Machine learning methods are mainly applied in evaluating students’ abilities, processing course feedback information, predicting and identifying high-risk students, and so on. The results show that the evaluation effect of machine learning is better, and it has great advantages in improving evaluation efficiency, saving evaluation cost and constructing scientific evaluation indexes. Conclusion: Currently, there are few researches on the application of machine learning in the evaluation of medical education and teaching. Existing researches have verified the feasibility of the application of machine learning in the evaluation of medical education and teaching, but its accuracy, generalization and effectiveness still need to be further improved.