Artificial intelligence (AI) in eye care uses computer algorithms, particularly machine learning and deep learning, to analyse data and support diagnosis, prognosis, and treatment decisions. It is especially powerful for image‑based tasks.
Current Uses
AI systems can detect diabetic retinopathy, macular degeneration, and glaucoma from retinal photographs or OCT scans with high sensitivity. They also assist in triage, screening, and monitoring disease progression.
- Automated screening programmes can extend access in primary care or community settings
- Decision‑support tools highlight suspicious regions for clinician review
- Predictive models estimate treatment response or risk of progression
- AI also optimises clinic workflows and research data analysis
Considerations
AI tools augment rather than replace clinicians and must be validated in diverse populations. Governance covers data privacy, bias, and accountability.
- Clinical oversight remains essential to interpret AI outputs
- Regulators increasingly classify some AI tools as medical devices
- Ongoing updates and monitoring are needed as algorithms and populations evolve
- Patient communication should explain how AI contributes to their care
