The Role of Artificial Intelligence in Teaching Ophthalmology Skills: A Systematic Review
In brief
AI tutoring lifts ophthalmology diagnostic skill by roughly one standard deviation
In seven small studies of 200 trainees, AI-based instruction produced markedly larger gains in disease recognition and diagnosis than traditional lectures, with effect sizes around 0.8 to over 2 standard deviations. Learners also rated AI patient simulations as comparable to human actors, but the evidence is limited and heterogeneous, so larger trials are needed to define AI's exact educational role.
- Journal
- Seminars in ophthalmology (Q2)
- Published
- 3 September 2026
- Study design
- Systematic review / meta-analysis of RCTs
- Evidence level
- Level 1, High (CEBM 1a)
- Authors
- Abu Bakar Butt, Rachel Leong, Michael Balas, Marko M Popovic
- PMID
- 42690629
- DOI
- 10.1080/08820538.2026.2725232
Why clinicians should know about it
- Picked for Ophthalmology (top studies of the week, 6 September 2026): High-quality evidence in a top journal
Abstract
BACKGROUND: Ophthalmology training requires visual interpretation, procedural skill, and supervised clinical reasoning, but trainee volume, faculty availability, and case mix constrain education. AI-enabled tools may support scalable instruction, assessment, and feedback. OBJECTIVE: To evaluate AI-enabled interventions for improving ophthalmology diagnostic, clinical reasoning, and surgical skills, and summarize knowledge acquisition, AI performance metrics, and learner perceptions. METHODS: This PROSPERO-registered review (CRD420251231199) followed PRISMA guidelines. Embase, Ovid MEDLINE, and Cochrane Library were searched through November 15, 2025. Eligible studies were observational or randomized trials in which trainees or clinicians performed ophthalmic diagnostic, clinical, or surgical tasks using AI-based instruction or assessment. Outcomes included diagnostic accuracy, knowledge, clinical reasoning, surgical skill, usability, satisfaction, and educational value. Risk of bias was assessed using ROBINS-I. Findings were synthesized narratively. RESULTS: Seven studies (200 participants) spanned image-based deep learning for diagnostic training, video-based deep learning for surgical assessment, and large language models for educational simulation. AI-tutored learners showed greater gains than lecture-based instruction in disease recognition and diagnosis (Cohen's d = 0.82, p = .016); an AI myopia system produced large gains in classification and lesion detection (d = 1.3-2.3) where lecture alone showed none (p = .16-0.63). AI-based patient simulation was rated comparably to human actors (p = .48). AI-derived surgical metrics distinguished attending from resident performance (AUC 0.55-0.998). CONCLUSION: AI-enabled interventions show promise as adjuncts to ophthalmology training, particularly for diagnostic learning and objective feedback. Evidence remains limited by small samples and heterogeneity, requiring larger, standardized studies to define AI's role.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.