Advances in the application of artificial intelligence in ophthalmic education and clinical training

TL;DR

Artificial intelligence is being used to improve ophthalmic education and training through knowledge assessment, surgical feedback, and patient education, though challenges remain.

Contribution

This paper reviews recent AI applications in ophthalmic education and highlights new tools for training and ethical challenges.

Findings

AI enables scalable knowledge assessment and rapid generation of educational materials.

Computer vision provides objective feedback for microsurgical training.

AI tools improve patient education accessibility and readability.

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.

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