Höhne Elena, Bauer Eva, Bauer Claus, Schäfer Valentin, Gotta Jennifer, Reschke Philipp, Vogl Thomas, Yel Ibrahim, Weimer Johannes, Wittek Agnes, Recker Florian
Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Frankfurt, Germany (E.H., J.G., P.R., T.V., I.Y.).
Department of Obstetrics and Prenatal Medicine, University Hospital Bonn, Bonn, Germany (E.B., A.W., F.R.).
Acad Radiol. 2025 Aug;32(8):4930-4938. doi: 10.1016/j.acra.2025.04.024. Epub 2025 Apr 28.
The integration of artificial intelligence (AI) into medical education presents significant opportunities for enhancing teaching methods and student learning outcomes. Despite its potential benefits, the implementation of AI in curricula remains limited and lacks standardized approaches.
This bicentric pilot study aims to examine the effectiveness of an innovative ultrasound course for medical students that combines AI-based teaching with blended e-learning, compared to traditional classroom lessons, to optimize educational practices.
This bicentric pilot study included medical students who were randomly assigned to an experimental group receiving AI-based blended e-learning for an ultrasound course or a control group receiving traditional classroom instruction. The curriculum consisted of two modules: lung ultrasound and Focused Assessment with Sonography for Trauma (FAST). The effectiveness of the interventions was evaluated using objective structured clinical examinations (OSCE) to assess ultrasound skills, administered as pre-tests and post-tests. Additionally, the quality of the ultrasound images obtained during the final assessment was rated using a standardized scoring system to further assess student competency.
50 clinical-phase medical students participated. OSCE results for both FAST and lung modules revealed no significant differences between groups at both pretest (pretest FAST p=0.722, pretest Lung p=0.062) and final exam (final exam FAST p=0.634, final exam lung p=0.843), with both cohorts achieving comparable improvements and nearly identical final scores, while ultrasound image evaluations confirmed similar outcomes (FAST images p=0.558 and lung images p=0.199) with excellent interrater reliability (ICC=0.993).
AI- and app-based learning methods in ultrasound education showed to be equally effective as traditional hands-on teaching for medical students in this study. Incorporating the permanently growing innovations auf AI into curricula can provide valuable tools for educators and students alike.
将人工智能（AI）融入医学教育为改进教学方法和提高学生学习成果带来了重大机遇。尽管具有潜在益处，但人工智能在课程中的应用仍然有限，且缺乏标准化方法。
本双中心试点研究旨在检验一门针对医学生的创新超声课程的有效性，该课程将基于人工智能的教学与混合式电子学习相结合，与传统课堂教学相比，以优化教育实践。
本双中心试点研究纳入了医学生，他们被随机分配到接受超声课程基于人工智能的混合式电子学习的实验组或接受传统课堂教学的对照组。课程包括两个模块：肺部超声和创伤重点超声评估（FAST）。使用客观结构化临床考试（OSCE）评估超声技能来评估干预措施的有效性，在课前和课后进行测试。此外，在最终评估期间获得的超声图像质量使用标准化评分系统进行评级，以进一步评估学生能力。
50名临床阶段医学生参与。FAST和肺部模块的OSCE结果显示，两组在课前（课前FAST p = 0.722，课前肺部p = 0.062）和期末考试（期末考试FAST p = 0.634，期末考试肺部p = 0.843）时均无显著差异，两个队列的改善程度相当，最终成绩几乎相同，而超声图像评估证实结果相似（FAST图像p = 0.558，肺部图像p = 0.199），评分者间信度极佳（ICC = 0.993）。
在本研究中，超声教育中基于人工智能和应用程序的学习方法对医学生而言与传统实践教学同样有效。将不断发展的人工智能创新纳入课程可为教育工作者和学生提供有价值的工具。
