In:
Insights into Imaging, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2020-12)
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1869-4101
DOI:
10.1186/s13244-020-00932-0
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2543323-4
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