Comparative educational effectiveness of AI generated images and traditional lectures for diagnosing chalazion and sebaceous carcinoma

Abstract

Abstract Sebaceous carcinoma is difficult to distinguish from chalazion due to their rarity and clinicians’ limited experience. This study investigated the potential of AI-generated image training to improve diagnostic skills for these eyelid tumors compared to traditional video lecture-based education. Students from Orthoptics, Optometry, and Vision Research (n = 55) were randomly assigned to either an AI-generated image training group or a traditional video lecture group. Diagnostic performance was assessed using a 50-image quiz before and after the intervention. Both groups showed significant improvement in overall diagnostic accuracy (p < 0.001), with no significant difference between groups (p = 0.124). In the AI group, all 25 chalazion images showed improvement, while only 6 out of 25 sebaceous carcinoma images improved. The video lecture group showed improvement in 19 out of 25 chalazion images and 24 out of 25 sebaceous carcinoma images. The proportion of images with improved accuracy was significantly higher in the AI group for chalazion (P = 0.022) and in the video group for sebaceous carcinoma (P < 0.001). These findings suggest that AI-generated image training can enhance diagnostic skills for rare conditions, but its effectiveness depends on the quality and quantity of patient images used for optimization. Combining AI-generated image training with traditional video lectures may lead to more effective educational programs. Further research is needed to explore AI’s potential in medical education and improve diagnostic skills for rare diseases.

Keywords

• AI-generated images
• Diagnostic skills
• Eyelid tumors
• Sebaceous carcinoma
• Chalazion
• Medical education