A review of AI for ophthalmic disease detection found that several tools have already received regulatory approval for commercial diabetic retinopathy screening, while broader use across eye disease remains limited by real-world implementation barriers. The review highlights strong diagnostic performance for diabetic retinopathy, diabetic macular oedema, glaucoma, age-related macular degeneration and retinopathy of prematurity, but notes that routine clinical translation still lags.

Why It Matters To Your Practice

• AI in ophthalmology is no longer theoretical: approved diabetic retinopathy screening tools are already on the market.

• These systems could help expand screening capacity, especially where ophthalmology access is limited.

• For clinicians, the near-term impact is most likely in image-based triage and detection workflows rather than fully autonomous management.

Clinical Implications

• Diabetic retinopathy is currently the clearest use case, with regulatory-cleared tools supporting screening programs.

• Foundation models and multimodal systems may improve generalisability across imaging platforms and patient populations.

• Emerging agentic AI could eventually combine detection, decision support and report generation in a single workflow.

Insights

• The article is a review, not a new prospective trial, so it summarises progress rather than proving outcome benefits in practice.

• Key barriers to adoption include transparency, bias, regulation, economics and fit with clinical workflows.

• The biggest evidence gap is prospective, real-world evaluation showing that performance holds up outside curated datasets.

The Bottom Line

• AI for diabetic retinopathy screening is already crossing into practice through approved tools.

• For most other ophthalmic applications, technical performance appears promising, but implementation evidence remains the bottleneck.

• Clinicians should watch for tools with regulatory clearance, real-world validation and clear workflow integration.