Impact of Artificial Intelligence on Diagnosing Eye Diseases – A Meta-analysis

Abstract

The application of artificial intelligence (AI) in the field of ophthalmology has garnered significant attention for its potential to enhance the accuracy of eye disease diagnosis. This systematic review and meta-analysis aimed to comprehensively assess the impact of AI on diagnosing eye diseases through the synthesis of existing research. A systematic search of electronic databases was conducted to identify relevant studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Eligible studies were those that reported the diagnostic accuracy of AI in ophthalmic image diagnosis. The standardized mean difference (SMD) and mean difference (MD) were utilised as the effect size measures to evaluate AI performance. A total of 18 studies meeting the inclusion criteria were selected for the quantitative synthesis. Further, the meta-analysis revealed that AI exhibited a substantial positive impact on the accuracy of diagnosing eye diseases. The overall SMD across various diagnostic parameters indicated a statistically significant improvement (SMD = 0.88, 95% confidence interval [CI]: 0.71–1.05). Moreover, the MD of diagnostic values demonstrated significant enhancements, with an overall MD of −10.2 (95% CI: −12.1 to −8.3). The selected studies consistently demonstrated that AI achieved high accuracy levels, reinforcing its potential as a valuable tool in ophthalmic diagnosis. This study provides significant evidence supporting the significant positive impact of AI on diagnosing eye diseases. The synthesis of the selected studies underscores the high accuracy achieved by AI in ophthalmic image diagnosis, as indicated by the substantial SMD and MD improvements. These findings highlight the promising role of AI in ophthalmology, offering the potential to revolutionise the field and improve patient care through enhanced diagnostic precision.