Assessing the quality and educational applicability of AI-generated anterior segment images in ophthalmology.

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 wit

SUBMITTER: Yang Y 

PROVIDER: S-EPMC12663094 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature