Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease...

Background: Artificial intelligence has significantly improved the diagnosis of retinal diseases using fundus and optical coherence tomography. However, the pathway between the accuracy of the models and their clinical application is still unclear. Objective: The objective of this systematic review is to overcome this gap by correlating the quality of methodology with clinical preparation in the current studies and to critically examine the translation readiness possibility. Methods: The PRISMA-based review clearly addresses the six research questions in an organized manner. A comprehensive search is conducted across six electronic databases, resulting in a total of 883 records. After rigorous screening, a final set of 43 fundus- and OCT-based articles published between 2020 and August 2025 that passed a comprehensive eligibility criterion was selected. The selected studies are assessed using the custom AI-specific risk-of-bias assessment tool and the CLAIM checklist to measure the quality of technical reporting. The reviewer's agreement is calculated using Cohen's kappa, interrater reliability, and the intraclass correlation coefficient. Results: Although convolutional and hybrid deep-learning-based studies are often reported to have an accuracy of more than 95.0%, only 14.3% are truly clinically validated, and 23.1% do not include code or implementation data details. Reviewers' agreement scores are from moderate to high (fundus-based ICC(2, 1) = 0.911, CI = [0.896, 0.923]) and (OCT based ICC(2, 1) = 0.643, CI = [0.422, 0.792]), supporting the reliability of the quality assessments. In order to combine these dimensions, we come up with a composite Technical-Clinical Integrity Index, which measures data transparency, rigor of validation, and reporting of performance. Conclusions: Retinal disease diagnosis has achieved high performance with low clinical transfer. This review provides the guidelines of a Translational Readiness Framework that can be used by researchers, clinicians, and health analytics teams to assess and implement credible artificial intelligence systems in ophthalmology.