Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.

Artificial intelligence (AI) has shown remarkable accuracy in the diagnosis of common ocular diseases such as diabetic retinopathy (DR), glaucoma, retinopathy of prematurity (ROP), and age-related macular degeneration (AMD), often matching or even outperforming expert clinicians. Despite these advancements, AI adoption in clinical settings remains limited due to key barriers. This systematic review evaluates 34 studies (2018-2025) highlighting AI's diagnostic performance (often >90% accuracy) while pointing out significant gaps in real-world deployment. We identify these persistent challenges through comprehensive analysis of current literature and propose actionable pathways to bridge the "last-mile gap" between research and clinical practice. This review pointed out three significant gaps in real-world deployment. These include 1) disjointed integration into clinical workflows, 2) lack of transparency in AI decision-making, and 3) poor generalizability across diverse populations. Our findings provide a framework for advancing AI implementation in ocular diagnostics to achieve equitable, scalable, and trustworthy solutions for global vision care.