LO
Psychiatrist — 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
Starts at 16 because before language models the automation in psychiatry was electronic prescribing with interaction checks, record templates and computer-scored questionnaires, all around the edges of the work. It rises from 2023 as language models could draft letters and summarise records, and again through 2024 and 2025 as ambient documentation entered clinics; adoption in psychiatry trails the rest of medicine because of consent and recording concerns. It flattens towards 31 because the core (examination, prescribing, risk and detention decisions) is held by licensing and mental health law, which capability alone does not move.
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
- 2026-10-25
- Basis of the task judgements
- 0 evidence-backed · 6 platform inference · 0 not enough evidence
- Verified events
- 0