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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Optometrists?
Task-level automation exposure for Optometrists: 10 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
27%
weighted task index
10 of 10 tasks scored
Median Salary
$136,570 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
42,790 people hold this job — size, not growth
Projected 2025–35
+9.5%
employment change
2,000 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$97,350 – $170,310
Highest medians
Alaska $170,310
Maryland $165,840
North Carolina $161,560
Delaware $161,270
New York $161,140
Lowest medians
Oklahoma $97,350
South Dakota $102,320
Idaho $103,390
Montana $104,280
Mississippi $108,070
State figures come from the 29-1041 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 27%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 59%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 25/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
10 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 0%
0 tasks
Medium exposure (≥ 35%) 21%
2 tasks
Low exposure (below 35%) 79%
8 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#337 · Core · 15% automatable
Examine eyes, using observation, instruments, and pharmaceutical agents, to determine visual acuity and perception, focus, and coordination and to diagnose diseases and other abnormalities, such as glaucoma or color blindness.
physical manipulation — Vision tests require hands-on patient examination
Test patient vision.
#338 · Core · 48% automatable
Analyze test results and develop a treatment plan.
visual inspection — Image interpretation assists; radiologist owns diagnosis
Analyze test data or images to inform diagnosis or treatment. Develop medical treatment plans.
#339 · Core · 17% automatable
Prescribe, supply, fit and adjust eyeglasses, contact lenses, and other vision aids.
decision support — Device prescription needs clinical assessment and accountability
Fit eyeglasses, contact lenses, or other vision aids. Prescribe assistive medical devices or related treatments.
#340 · Core · 22% automatable
Prescribe medications to treat eye diseases if state laws permit.
decision support — Prescribing medications is licensed high-stakes judgment
Prescribe medications.
#341 · Core · 30% automatable
Educate and counsel patients on contact lens care, visual hygiene, lighting arrangements, and safety factors.
interpersonal — Caregiver technique training is live and relational
Train patients, family members, or caregivers in techniques for managing disabilities or illnesses.
#343 · Core · 12% automatable
Remove foreign bodies from the eye.
physical manipulation — Hands-on treatment of acute conditions
Treat acute illnesses, infections, or injuries.
#344 · Core · 30% automatable
Provide patients undergoing eye surgeries, such as cataract and laser vision correction, with pre- and post-operative care.
decision support — Post-surgical monitoring is hands-on clinical observation
Monitor patients following surgeries or other treatments.
#342 · Core · 48% automatable
Consult with and refer patients to ophthalmologist or other health care practitioner if additional medical treatment is determined necessary.
decision support — Clinical referral judgment needs licensed human accountability
Collaborate with healthcare professionals to plan or provide treatment. Refer patients to other healthcare practitioners or health resources.
#345 · Core · 25% automatable
Prescribe therapeutic procedures to correct or conserve vision.
decision support — Prescribing therapy requires licensed clinical accountability
Prescribe treatments or therapies.
#346 · Core · 15% automatable
Provide vision therapy and low-vision rehabilitation.
decision support — Chronic disease management needs clinical judgment
Treat chronic diseases or disorders.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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