ArticleScientific reports2025
Assessing the quality and educational applicability of AI-generated anterior segment images in ophthalmology.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Comparative Evaluation of Deep-Reasoning Large Language Models for Ophthalmic Emergencies.Ophthalmology science · 2026Article
- Comparative performance of contemporary multimodal large language models in retinal imaging question answering.Frontiers in medicine · 2026Article
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Making Chatbots more human: deep reasoning large language models in ophthalmology.Frontiers in medicine · 2025Review
- Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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