Artificial intelligence applications in refractive error management: A systematic review and meta-analysis

Artificial intelligence (AI) has transformed healthcare, and is becoming increasingly useful in eye care. We conducted a systematic review and meta-analysis of the use of AI in the diagnosis, detection, prediction, progression, and treatment of refractive errors (REs). The study adhered to the PRISMA checklist to ensure transparent reporting. The following databases were searched from inception to January 2025, with an English language restriction: PubMed, Web of Science, Embase, Scopus, Cochrane Library and Google Scholar. Two independent reviewers performed study screening, data extraction, and quality assessment, with a third author resolving discrepancies. All original studies on the use of AI techniques in RE were identified and the effectiveness of these techniques was compared. A critical appraisal was conducted using the QUADAS-2 risk-of-bias tool. A meta-analysis was performed using R software (version 4.5.0). Of 6,288 records retrieved, 45 met eligibility for systematic review, with 19 included in meta-analysis. Among these 45 studies, 55.5% (25/45) applied deep learning (DL) approaches, while 44.4% (20/45) employed machine learning (ML) techniques. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary of receiver operating characteristic (SROC) for detection and/or diagnosis studies were 0.94 (95%CI, 0.90-0.97), 0.96 (95%CI, 0.92-0.98), 382.56 (95% CI 111.91 -1307.77) and 0.98 (95%CI, 0.91-0.97), respectively. For prediction of REs, the pooled sensitivity, specificity, DOR, and SROC were 0.87 (95%CI, 0.73-0.94), 0.96 (95%CI, 0.90-0.980), 159.94 (95% CI, 40.17-636.85) and 0.96 (95%CI, 0.85-0.95), respectively. Among studies focused on progression, performance metrics ranged from AUC = 0.845-0.99, R² = 0.613-0.964, and MAE = 0.119D-0.49D. In treatment studies, performance varied more widely, with AUC values between 0.60–0.94 and MAE from 0.17D-0.54D. Collectively, AI technologies, particularly DL and ML, achieved high diagnostic and predictive accuracy in RE management. Future research should focus on developing generalizable models trained on diverse datasets to ensure broad clinical relevance.Author summary: Refractive errors, such as nearsightedness, farsightedness, and astigmatism, are common vision problems that affect people of all ages. They can usually be corrected with glasses, contact lenses, or surgery, but early and accurate detection is key to effective treatment. In recent years, AI has become a valuable tool in improving how these conditions are diagnosed, monitored, and managed. We investigate how AI is being used to detect, predict, track progression, and help treat these conditions. We analyzed 45 studies, of which 19 were included in a detailed meta-analysis. Over half of the included studies used DL, a type of AI that mimics the human brain, while the others used ML, an AI approach where computers identify patterns from data. We found that AI tools were very accurate at diagnosing and predicting refractive errors, with high sensitivity (correctly identifying those with the condition) and specificity (correctly identifying those without it). Further, the AI was also useful in tracking changes in vision over time and in helping guide treatment choices, although performance varied more in those areas. Overall, AI shows strong potential to improve how we detect and manage refractive errors. Future work should focus on developing AI systems that work well across different populations and clinical settings.