Assessment of predictive value of artificial intelligence for ophthalmic diseases using electronic health records: A systematic review and meta-analysis

Purpose

The application of artificial intelligence (AI) in ophthalmology has shown significant promise across various clinical domains. This study addresses the need for assessing the predictive value of AI models utilizing electronic health records (EHRs) for diagnosis, prognostication and management of ocular diseases.

Methods

A search was conducted using Ovid MEDLINE, Ovid EMBASE, and Cochrane Central for relevant studies published between January 2010 to February 2023 on predictive value of AI algorithms in ophthalmic EHRs. The study followed the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA) guidelines, with a protocol registered on Prospero (registration number: CRD42022303128). A bivariate random effects model was used to perform the meta-analysis. The ROBINS-I tool was used to assess methodological quality and applicability of the included studies.

Results

Out of 4968 initial records, 41 studies met the inclusion criteria, comprising a total of 639,637 patients, with an average disease prevalence of 11%. The studies exhibited a diagnostic odds ratio of 18.527 (95% CI: 9.654–35.556), sensitivity of 0.811 (95% CI: 0.751−0.859), specificity of 0.812 (95% CI: 0.736−0.87) and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) moderate. Likelihood ratios (LR+ and LR−) were 4.316 (95% CI: 2.938–6.339) and 0.233 (95% CI: 0.169−0.322), respectively. False positive rate was 0.188 (95% CI: 0.13−0.264). Inter-rate concordance for ROBINS-I scoring had a kappa score of 0.83. Out of the 41 studies, 22 had an overall low risk of bias, and 19 had a moderate risk of bias. There was a low to moderate quality of body of evidence for the reported outcomes.

Conclusion

This meta-analysis affirms the substantial potential of AI models utilizing EHRs for predictive modeling and clinical management of ocular diseases. Future research should emphasize external validation and standardized reporting for better implementation of AI in ophthalmic practice.