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. To evaluate AI-enabled interventions for improving ophthalmology diagnostic, clinical reasoning, and surgical skills, and summarize knowledge acquisition, AI performance metrics, and learner perceptions. 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. 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). 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.
Abu Bakar Butt, Rachel Leong, Michael Balas (7022801) et al.· Figshare· 0 citations
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. To evaluate AI-enabled interventions for improving ophthalmology diagnostic, clinical reasoning, and surgical skills, and summarize knowledge acquisition, AI performance metrics, and learner perceptions. 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. 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). 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.
Abu Bakar Butt, Rachel Leong, Michael Balas (7022801) et al.· Figshare· 0 citations
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.
Abu Bakar Butt, Rachel Leong, Michael Balas et al.· Seminars in Ophthalmology· 0 citations
