Optometrists

Optometrists do many hands-on tasks, but AI is beginning to help with some of them. For example, computer algorithms can now read eye images (like retinal photos) and flag disease. Studies show AI models can detect glaucoma or diabetic eye disease from scans with accuracy as good as or better than human doctors ^{[1] [2]}.

In practice, FDA-approved tools (like LumineticsCore and EyeArt) automate diabetes retinopathy screening in some clinics ^[2]. AI is also used to optimize vision correction: one recent study used machine learning to design contact lenses, improving lens performance by 24–50% compared to traditional methods ^[2]. There are even AI telemedicine tools: for example, an “AI nurse” phone-call system after cataract surgery matched doctors’ triage in 83–100% of cases ^[2].

That said, many core tasks still need humans. Things like gently removing a speck from a patient’s eye or guiding vision-therapy exercises require a skilled doctor’s hands and judgment; there are no robots for those parts yet. In short, AI today is mostly an assistant that speeds up image analysis and routine checks, while optometrists focus on complex care and patient interaction ^{[2] [2]}.

There are strong reasons both for and against rapid AI use in optometry. On the plus side, there is a global shortage of eye doctors: in low-income regions one review found optometrists meet only a small fraction of need (over 30% of patients travel 100+ km for care) ^[2]. In those settings, AI for screening could extend services more cheaply and speed early detection ^{[2] [2]}.

Also, automating routine checks can save busy clinics time and money (one report notes AI screening could “drive down the system-level cost of care” for glaucoma/retina diseases ^[2]).