Pediatricians

Healthcare

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.29

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 69% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Examine patients or order, perform, and interpret diagnostic tests to obtain information on medical condition and determine diagnosis.

O*NET Task ID 7819

0.5
Examine children regularly to assess their growth and development.

O*NET Task ID 7820

0.5
Prescribe or administer treatment, therapy, medication, vaccination, and other specialized medical care to treat or prevent illness, disease, or injury in infants and children.

O*NET Task ID 7821

0.5
Collect, record, and maintain patient information, such as medical history, reports, or examination results.

O*NET Task ID 7822

0.5
Advise patients, parents or guardians, and community members concerning diet, activity, hygiene, and disease prevention.

O*NET Task ID 7823

0.5
Treat children who have minor illnesses, acute and chronic health problems, and growth and development concerns.

O*NET Task ID 7824

0.5
Explain procedures and discuss test results or prescribed treatments with patients and parents or guardians.

O*NET Task ID 7825

0.5
Monitor patients' conditions and progress and reevaluate treatments as necessary.

O*NET Task ID 7826

0.5
Plan and execute medical care programs to aid in the mental and physical growth and development of children and adolescents.

O*NET Task ID 7827

0.5
Refer patient to medical specialist or other practitioner when necessary.

O*NET Task ID 7828

0.5
Operate on patients to remove, repair, or improve functioning of diseased or injured body parts and systems.

O*NET Task ID 7832

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information