Abstract Text-to-image (T2I) artificial intelligence models are being increasingly explored in medical education, yet their utility in ophthalmology remains unclear. Slit-lamp anterior segment photography, as a cornerstone of ophthalmic training, provides an ideal context for evaluation. We assessed 40 cases of anterior segment disease. The text descriptions were generated using GPT-4o, and the corresponding images were synthesized via Sora Turbo. Readability was analysed with the Flesch Reading Ease (FRE), Flesch‒Kincaid Grade Level (FKGL), and Gunning Fog Scale (GFS). Twenty ophthalmologists (10 juniors, 10 seniors) rated image-text pairs across five dimensions—text accuracy, image reliability, recognizability, educational value, and generation stability—using a 5-point Likert scale. Entities with distinct morphological features, such as cataracts and subconjunctival haemorrhages, received the highest total scores, whereas those with entropion and corneal foreign bodies scored the lowest. Readability analysis indicated advanced text complexity. Senior ophthalmologists consistently provided lower ratings than junior clinicians did, highlighting expertise-related differences in perceived educational value. Sora Turbo can generate clinically useful anterior segment images for educational purposes, particularly for pathologies with prominent morphological features. This first systematic evaluation in ophthalmology demonstrates the promise of AI-generated atlases as scalable teaching resources for early-stage trainees while emphasizing the need for expert validation and ethical oversight before integration into formal curricula.
Yang et al. (2025) studied this question.
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