Evidence map›Paper›PMID 33226480›Full record

ArticleInsights into imaging2020

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 and 1 more

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Article in Insights into imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  15. Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Chi-Tung ChengDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Chih-Chi ChenDepartment of Rehabilitation and Physical Medicine, Chang Gung Memorial Hospital, Chang Gung University, Linkou, Taoyuan, Taiwan.
Chih-Yuan FuDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Chung-Hsien ChaouDepartment of Emergency Medicine, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan.
Yu-Tung WuDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Chih-Po HsuDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Chih-Chen ChangDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Linkou, Taiwan.
I-Fang ChungInstitute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
Chi-Hsun HsiehDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan.
Ming-Ju HsiehSchool of Medicine, Chang Gung University, Taoyuan, Taiwan.
Chien-Hung LiaoDepartment of Traumatology and Emergency Surgery, Chang Gung Memorial Hospital, Chang Gung University, 5 Fu-Hsing Street, Kwei-Shan District, Taoyuan, Taiwan. surgymet@gmail.com.

Funding

Chang Gung Memorial Hospital, Linkou CIRPG3H0021Chang Gung Memorial Hospital, Linkou CMRPG 3J631Chang Gung Memorial Hospital, Linkou CMRPGJ1121
6 · The paper itself

Abstract

backgroundWith 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.

resultsThe 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.

conclusionThe study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Indexed as

Artificial intelligenceDeep learningFractureMedical image educationPersonalized education

Identifiers

PMID33226480
PMCPMC7683624

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.