ReviewFrontiers in medicine2025
Advances in the application of artificial intelligence in ophthalmic education and clinical training.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Design and application of a web-based intelligent ophthalmic image analysis teaching platform.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ophthalmic education faces increasing demands due to rising disease burden, prolonged training pathways, and unequal access to educational resources. Artificial intelligence (AI) is increasingly used to support ophthalmic training across multiple educational stages. This review summarizes recent evidence on AI applications in ophthalmic education, focusing on theoretical knowledge assessment and content generation, the objective evaluation of microsurgical skills, AI-assisted development of clinical diagnostic reasoning, and patient education. Large language models enable scalable knowledge assessment and rapid generation of structured educational materials, while computer vision and sensor-based technologies provide objective, quantitative feedback for microsurgical training. AI-assisted diagnostic and simulation systems support clinical reasoning through visual explanations and diverse virtual cases, and AI-driven tools improve the accessibility and readability of patient's education materials. However, ethical and practical challenges-including model hallucination, data bias, privacy risks, and implementation barriers-limit widespread adoption. Addressing these issues through robust governance and effective human-AI collaboration is essential for safe, equitable, and high-quality ophthalmic education.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.