International Journal of Clinical Research
International Journal of Clinical Research. 2024; 8: (12) ; 10.12208/j.ijcr.20240513 .
总浏览量: 88
1 南京大学医学院附属鼓楼医院 江苏南京
2 南京鼓楼医院集团仪征医院 江苏仪征
*通讯作者： 牡丹,单位： 南京大学医学院附属鼓楼医院 江苏南京 南京鼓楼医院集团仪征医院 江苏仪征；
目的 探讨人工智能辅助教学在实习医生冠状动脉计算机体层血管成像（computed tomography angiography，CTA）影像诊断教学中的应用及效果。方法 选取2021年6月—2023年6月在南京鼓楼医院医学影像科实习的医学影像学专业的60名大学五年级的学生作为研究对象，随机分为实验组和对照组，对照组采用传统教学模式进行冠状动脉CTA诊断实习培训，实验组学生接受人工智能辅助教学方式进行实习培训。对比两组学生实习结束后的理论成绩、学习效率和临床实践能力。结果 实验组学生图像后处理时间（21.75秒 vs. 321.80秒， P<0.001）和报告书写时间（307.72秒vs. 325.67秒，P<0.05）显著少于对照组，理论成绩（86.93分vs. 82.30分，P<0.001）、图像后处理质量（4.60分vs. 4.05分， P<0.001）和报告质量（4.38分vs. 4.04分，P<0.001）均显著高于对照组。结论 人工智能辅助冠状动脉CTA诊断教学有助于提升学生的学习效率，巩固理论知识，提高冠脉CTA诊断能力，使教学效果得到全面提高，值得推广应用。
Objective This study aimed to explore the application and effect of artificial intelligence-assisted teaching in coronary computed tomography angiography (CTA) diagnosis for interns. Methods Sixty students of grade 5 majoring in medical imaging who were interning in the medical imaging department of Nanjing Drum Tower Hospital from June 2021 to June 2023 were selected as the research subjects. They were randomly divided into an experimental group and a control group. Students in the experimental group were trained on coronary CTA diagnosis using the artificial intelligence-assisted teaching mode, while students in the control group were trained using the traditional teaching mode. The theoretical scores, learning efficiency and clinical practice abilities of students in the two groups were compared after the internship. Results The image post-processing time (21.75 sec vs. 321.80 sec, P<0.001) and report writing time (307.72 sec vs. 325.67 sec, P<0.05) of students in the experimental group were significantly shorter than those in the control group, and their theoretical scores (86.93 vs. 82.30, P<0.001), image post-processing quality (4.60 vs. 4.05, P<0.001) and reporting quality (4.38 vs. 4.04, P<0.001) were significantly higher than those in the control group. Conclusion AI technology-assisted coronary CTA diagnostic teaching helped improve learning efficiency, consolidate theoretical knowledge and improved coronary CTA diagnostic capabilities of students. The assistance of AI can comprehensively improve the teaching effect of coronary CTA diagnosis and is worthy of promotion and application.
[1] 熊瑶, 陈敏.人工智能在医疗领域应用现状探讨[J].医学信息学杂志, 2018, 39(4):24-28.
[2] 倪炯, 王培军. 医学影像人工智能的现状与未来[J].中华医学杂志, 2021,101(7):455-457.
[3] 谢武桃, 金盛辉, 毕朝虎等. AI背景下影像学专业实习教学模式的探讨[J]. 医学理论与实践, 2023, 36(8):1426-1429.
[4] 李新春, 王鹏, 苏曦, 等. 人工智能辅助教学在医学影像教学中的应用前景[J]. 中国继续医学教育, 2021, 13(4):86-89.
[5] Mir MM, Mir GM, Raina NT, et al. Application of artificial intelligence in medical education: current scenario and future perspectives[J]. J Adv Med Educ Prof. 2023, 11(3):133-140.
[6] 萧毅, 刘士远. 医学影像人工智能进入深水区后的思考[J]. 中华放射学杂志, 2019, 53(1):2-5.
[7] 王国杰，杨思怡，谢丽丽，等. 人工智能在医学影像专业教学中的应用探索[J]. 中国高等医学教育, 2024, 2:13-14.
[8] Wang C, Xie H, Wang S, et al. Radiological education in the era of artificial intelligence: A review[J]. Medicine (Baltimore). 2023, 102(1):e32518.
[9] Awan O, Dey C, Salts H, et al. Making learning fun: gaming in radiology education[J]. Acad Radiol. 2019, 26(8):1127-1136.
[10] Barragan-Montero A, Javaid U, Valdes G, et al. Artificial intelligence and machine learning for medical imaging: A technology review[J]. Physica Medica, 2021, 83:242-256.
[11] Cheng CT, Chen CC, Fu CY, et al. Artificial intelligence-based education assists medical students' interpretation of hip fracture[J]. Insights Imaging. 2020, 11(1):119.
[12] 严陈晨, 麦筱莉, 辛小燕, 等. 人工智能技术在医学影像实习教学中的探索[J]. 江苏卫生事业管理, 2021, 32(11): 1534-1538.