AI in eye care: what it can do today

In eye care, AI usually means software that finds patterns in images and measurements. The most common inputs are retinal photos, OCT scans, and sometimes visual field results. The output is not a diagnosis in the human sense. It is a probability score, a classification, or a prompt that suggests additional review.

A useful way to think about AI is that it excels at repeatable perception tasks: detecting visual features that look like known examples. It is weaker at context: symptoms, medical history, medication effects, and how a finding fits into a broader clinical story. That division explains why most real deployments use AI for screening and triage, not for final decisions.

Screening programs work when large numbers of people need evaluation, but specialists are limited. In those settings, the problem is not that clinicians lack skill. The problem is throughput. AI can help by prioritizing who needs faster human review and who can safely remain on routine intervals.

The best use cases are narrow and well defined: a specific disease target, a standardized image type, and a clear action that follows the result. When these pieces are present, AI can improve access and reduce delays. When the target is vague, the result tends to be a tool that looks impressive but does not change outcomes.

Every screening system trades false positives and false negatives. In eye care, a false negative can be serious if it delays care for a time sensitive condition. A false positive can be costly if it generates unnecessary visits and anxiety. The right balance depends on the disease, the population, and the local care pathway.

AI can also generalize poorly. A model trained on one camera, one population, or one clinic workflow may perform differently elsewhere. Lighting, pupil size, media opacity like cataract, and coexisting disease can all reduce image quality and shift performance. This is why real world validation matters more than marketing claims.

A practical way to evaluate an AI feature is to ask what changes as a result. Does it change the timing of referral, the need for additional testing, or the monitoring interval? If the answer is unclear, the tool may be more of a demonstration than a clinically integrated system.

It also helps to ask about oversight. Who reviews borderline results? What is the plan when the algorithm fails to grade an image? What are the local false positive rates, and how are patients informed? These are not technical questions. They are safety questions.