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Enhancing medical students’ diagnostic accuracy of infectious keratitis with AI-generated images

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Publicado en:BMC Medical Education vol. 25 (2025), p. 1-9
Autor principal: Xie, Wenjia
Otros Autores: Yuan, Zhouhang, Si, Yuxuan, Huang, Zhengxing, Li, Yingming, Wu, Fei, Yu-Feng, Yao
Publicado:
Springer Nature B.V.
Materias:
United States > US
Infections
Ophthalmology
Accuracy
Medical education
Datasets
Photographs
Disease
Cornea
Diagnostic tests
Medical personnel
Diffusion models
Medical students
Patients
Artificial intelligence
Herpes viruses
Classification
Viral infections
Variance analysis
Calculators
Student Characteristics
Educational Resources
Teaching Methods
Sample Size
Statistical Significance
Educational Change
Photography
Medical Evaluation
Correlation
Thinking Skills
Effect Size
Statistical Analysis
Visual Impairments
Acceso en línea:Citation/Abstract
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Resumen:BackgroundDeveloping students’ ability to accurately diagnose various types of keratitis is challenging. This study aims to compare the effectiveness of teaching methods—real cases, artificial intelligence (AI)-generated images, and real medical images—on improving medical students’ diagnostic accuracy of bacterial, fungal, and herpetic keratitis.Methods97 consecutive fourth-year medical students who had completed basic ophthalmology educational courses were included. The students were divided into three groups: 30 students in the group (G1) using the real cases for teaching, 37 students in the group (G2) using AI-generated images for teaching, and 30 students in the group (G3) using real medical images for teaching. The G1 group had a 1-hour study session using five real cases of each type of infectious keratitis. The G2 group and the G3 group each experienced a 1-hour image reading sessions using 50 AI-generated or real medical images of each type of infectious keratitis. Diagnostic accuracy for three types of infectious keratitis was assessed via a 30-question test using real patient images, compared before and after teaching interventions.ResultsAll teaching methods significantly improved mean overall diagnostic accuracy. The mean accuracy improved from 42.03 to 67.47% in the G1 group, from 42.68 to 71.27% in the G2 group, and from 46.50 to 74.23% in the G3 group, respectively. The mean accuracy improvement was highest in the G2 group (28.43%). There were no statistically significant differences in mean accuracy or accuracy improvement among the 3 groups.ConclusionsAI-generated images significantly enhance the diagnostic accuracy for infectious keratitis in medical students, performing comparably to traditional case-based teaching and real patient images. This method may standardize and improve clinical ophthalmology training, particularly for conditions with limited educational resources.
ISSN:1472-6920
DOI:10.1186/s12909-025-07592-y
Fuente:Healthcare Administration Database