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. 2025 Jul 9;25(1):1027.
doi: 10.1186/s12909-025-07592-y.

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

Wenjia Xie  1 , Zhouhang Yuan  1   2 , Yuxuan Si  1   2 , Zhengxing Huang  2 , Yingming Li  3 , Fei Wu  2 , Yu-Feng Yao  4   5
Affiliations

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

Wenjia Xie et al. BMC Med Educ. .

Abstract

Background: Developing 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.

Methods: 97 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.

Results: All 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.

Conclusions: AI-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.

Keywords: AI-Generated images; Artificial intelligence; Infectious keratitis; Medical education.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Informed consent was obtained from all participants before the study, and the medical students in this study were informed of the course arrangement and tests in advance. No personally identifiable patient information was included in the teaching materials. All the experiments in this study were conducted in accordance to the Declaration of Helsinki. This study was approved by the institutional review board of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Approval No.2024 − 0154). Clinical trial number: not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The framework of generative model. Legend: The model consists of a diffusion model used for image generation and two encoders used as generation conditions. In the process of generation, giving images and text as prompts can improve the stability of the generation process
Fig. 2
Fig. 2
Examples of artificial intelligent-generated infectious keratitis images. Legend: (A) bacterial keratitis, (B) fungal keratitis, (C) herpes simplex keratitis
Fig. 3
Fig. 3
Mean accuracies before and after different teaching methods. Legend: The mean (SD) accuracies significantly improved after teaching interventions in all groups (**P < 0.001, paired t-test)
See this image and copyright information in PMC

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