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

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.

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.

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.

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.