LO
Optometrist — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 4 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
Starts at 22 because autorefractors, automated visual-field tests and digital retinal cameras were routine before this chart begins, while an optometrist still read the images and wrote the prescription. It climbs as regulators authorise autonomous AI to detect diabetic eye disease, payers fund automated retinal analysis and national programmes pilot AI grading, and stays in the lower middle because rules keep the prescription tied to a full examination and screening bodies have not all adopted AI grading.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-10-01
- Basis of the task judgements
- 2 evidence-backed · 3 platform inference · 0 not enough evidence
- Verified events
- 8