Cheng Chi-Tung, Chen Chih-Chi, Fu Chih-Yuan, Chaou Chung-Hsien, Wu Yu-Tung, Hsu Chih-Po, Chang Chih-Chen, Chung I-Fang, Hsieh Chi-Hsun, Hsieh Ming-Ju, Liao Chien-Hung
Department of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan.
Insights Imaging. 2020 Nov 23;11(1):119. doi: 10.1186/s13244-020-00932-0.
With recent transformations in medical education, the integration of technology to improve medical students' abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees' medical image learning.
We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy.
The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group.
The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.
随着医学教育的近期变革，整合技术以提高医学生的能力已变得可行。人工智能（AI）已对医疗保健的多个方面产生影响。然而，很少有研究关注医学教育。我们进行了一项人工智能辅助教育研究，并证实人工智能可以加速学员的医学图像学习。
我们开发了一个基于人工智能的医学图像学习系统，以突出骨盆平片上的髋部骨折。30名医学生被分为传统（CL）组和人工智能辅助学习（AIL）组。在CL组中，参与者接受了学习前测试和学习后测试。在AIL组中，参与者在学习后测试前接受了另一次人工智能辅助教育测试。然后，我们分析了诊断准确性的变化。
两组的学习前表现相当。在CL组中，学习后准确率（78.66±14.53）高于学习前准确率（75.86±11.36），但无显著差异（p = 0.264）。AIL组表现出显著改善。有AI辅助的分数（88.87±5.51）显著高于学习前分数（75.73±10.58，p < 0.01）。此外，学习后分数（84.93±14.53）优于学习前分数（p < 0.01）。AIL组的准确率提高显著高于CL组。
该研究证明了人工智能增强医学教育的可行性。将人工智能整合到医学教育中需要从研究、临床和教育角度进行动态协作。
