Emergency Medicine Physicians

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: ILOPublished: 2025

    0.27

    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, 74% score at or above this value.

    Source dataset

  • Data source: OpenAIPublished: 2023

    0.353

    0.000Range of values carried here0.844
    Scale, basis and source

    Human-rater β, 0–1 as published

    Mapped onto O*NET tasks

    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

Show 3 hidden tasks

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
Collect and record patient information, such as medical history or examination results, in electronic or handwritten medical records.

O*NET Task ID 22717

1.0
Analyze records, examination information, or test results to diagnose medical conditions.

O*NET Task ID 22715

0.5
Assess patients' pain levels or sedation requirements.

O*NET Task ID 22716

0.5
Communicate likely outcomes of medical diseases or traumatic conditions to patients or their representatives.

O*NET Task ID 22718

0.5
Conduct primary patient assessments that include information from prior medical care.

O*NET Task ID 22719

0.5
Consult with hospitalists and other professionals, such as social workers, regarding patients' hospital admission, continued observation, transition of care, or discharge.

O*NET Task ID 22720

0.5
Direct and coordinate activities of nurses, assistants, specialists, residents, and other medical staff.

O*NET Task ID 22721

0.5
Discuss patients' treatment plans with physicians and other medical professionals.

O*NET Task ID 22722

0.5
Evaluate patients' vital signs or laboratory data to determine emergency intervention needs and priority of treatment.

O*NET Task ID 22723

0.5
Identify factors that may affect patient management, such as age, gender, barriers to communication, and underlying disease.

O*NET Task ID 22724

0.5

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