Artificial intelligence-based education assists medical students’ interpretation of hip fracture

Chi Tung Cheng, Chih Chi Chen, Chih Yuan Fu, Chung Hsien Chaou, Yu Tung Wu, Chih Po Hsu, Chih Chen Chang, I. Fang Chung, Chi Hsun Hsieh, Ming Ju Hsieh, Chien Hung Liao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: 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. Materials: 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. Results: 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. Conclusion: 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.

Original languageEnglish
Article number119
JournalInsights into Imaging
Volume11
Issue number1
DOIs
StatePublished - Dec 2020

Keywords

  • Artificial intelligence
  • Deep learning
  • Fracture
  • Medical image education
  • Personalized education

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