Artificial Intelligence for Clinical Decision-Making in Retinal Disorders: From Screening and Diagnosis to Treatment and Longitudinal Management.

Abstract

Artificial intelligence (AI) in retinal imaging has expanded from image classification to multimodal interpretation, longitudinal prediction, and clinically oriented decision support. This narrative review focuses on four complementary clinical settings: diabetic retinopathy (DR) screening and referral, diabetic macular edema (DME) treatment assessment, neovascular age-related macular degeneration (nAMD) retreatment and longitudinal monitoring, and inherited retinal disease (IRD) diagnosis, genotype-phenotype support, progression modeling, and trial enrichment. We organize the evidence around disease-specific decision points, relevant data modalities, and the requirements for multimodal and longitudinal integration. Evidence is strongest for DR screening, supported by prospective and real-world validation, whereas treatment-oriented applications in DME and nAMD and multimodal diagnostic or prognostic applications in IRDs remain less consistently validated. We highlight the gap between model performance and clinical utility and propose a cautious translational roadmap emphasizing external validation, calibration, uncertainty handling, workflow integration, prospective evaluation, and accountable deployment.