โก Quick Summary
This study evaluated the quality and educational applicability of AI-generated anterior segment images in ophthalmology, utilizing GPT-4o for text descriptions and Sora Turbo for image synthesis. The findings suggest that these AI-generated images can serve as valuable educational resources, particularly for conditions with distinct morphological features.
๐ Key Details
- ๐ Dataset: 40 cases of anterior segment disease
- ๐งฉ Technology: Text descriptions by GPT-4o, images by Sora Turbo
- ๐ Readability Analysis: Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Scale (GFS)
- ๐ฉโโ๏ธ Participants: 20 ophthalmologists (10 juniors, 10 seniors)
- โญ Evaluation Criteria: Text accuracy, image reliability, recognizability, educational value, generation stability
๐ Key Takeaways
- ๐ AI-generated images can enhance ophthalmic education, especially for conditions like cataracts and subconjunctival hemorrhages.
- ๐ก Readability analysis revealed advanced text complexity, indicating a need for simplification.
- ๐ฉโโ๏ธ Senior ophthalmologists rated the educational value lower than junior clinicians, highlighting expertise-related differences.
- ๐ High scores were achieved for entities with distinct morphological features.
- ๐ This study represents the first systematic evaluation of AI-generated images in ophthalmology.
- ๐ ๏ธ Sora Turbo demonstrated the capability to produce clinically useful images for educational purposes.
- ๐ Expert validation and ethical oversight are essential before integrating these resources into formal curricula.
๐ Background
The integration of artificial intelligence in medical education is gaining momentum, yet its specific applications in fields like ophthalmology remain underexplored. Slit-lamp anterior segment photography is a fundamental aspect of ophthalmic training, making it an ideal context for assessing the potential of AI-generated educational materials.
๐๏ธ Study
This study involved a systematic evaluation of 40 cases of anterior segment disease, where text descriptions were generated using GPT-4o and images were synthesized via Sora Turbo. The research aimed to determine the quality and educational applicability of these AI-generated resources in ophthalmology.
๐ Results
The results indicated that entities with distinct morphological features, such as cataracts and subconjunctival hemorrhages, received the highest ratings across various dimensions. In contrast, conditions like entropion and corneal foreign bodies scored lower. The readability analysis revealed that the generated text was complex, suggesting a need for adjustments to enhance understanding.
๐ Impact and Implications
The findings of this study highlight the potential of AI-generated atlases as scalable teaching resources for early-stage trainees in ophthalmology. By providing high-quality visual aids, these technologies could significantly enhance the learning experience. However, the necessity for expert validation and ethical considerations cannot be overstated, ensuring that such resources are effectively integrated into educational curricula.
๐ฎ Conclusion
This study underscores the promising role of AI in ophthalmology education, particularly through the use of AI-generated images. As we continue to explore these technologies, it is crucial to prioritize expert oversight and ethical standards to maximize their educational value. The future of ophthalmic training could be greatly enriched by these innovative resources, paving the way for improved learning outcomes.
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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), FleschโKincaid 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.
Author: [‘Yang Y’, ‘Bai L’, ‘Ren Y’, ‘Lin X’]
Journal: Sci Rep
Citation: Yang Y, et al. Assessing the quality and educational applicability of AI-generated anterior segment images in ophthalmology. Assessing the quality and educational applicability of AI-generated anterior segment images in ophthalmology. 2025; 15:42778. doi: 10.1038/s41598-025-27020-x