Radiologists

Healthcare

AI exposure

  • Data source: BLSPublished: '26.08

    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: '26.03

    0.199

    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: '25

    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

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 1 hidden task

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
Participate in quality improvement activities including discussions of areas where risk of error is high.

O*NET Task ID 17231

0.5
Participate in continuing education activities to maintain and develop expertise.

O*NET Task ID 17234

0.5
Develop treatment plans for radiology patients.

O*NET Task ID 17235

0.5
Establish or enforce standards for protection of patients or personnel.

O*NET Task ID 17236

0.5
Review or transmit images and information using picture archiving or communications systems.

O*NET Task ID 17239

0.5
Recognize or treat complications during and after procedures, including blood pressure problems, pain, oversedation, or bleeding.

O*NET Task ID 17241

0.5
Prepare comprehensive interpretive reports of findings.

O*NET Task ID 17242

0.5
Obtain patients' histories from electronic records, patient interviews, dictated reports, or by communicating with referring clinicians.

O*NET Task ID 17243

0.5
Confer with medical professionals regarding image-based diagnoses.

O*NET Task ID 17245

0.5
Instruct radiologic staff in desired techniques, positions, or projections.

O*NET Task ID 17246

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

Recent Changes Affecting This Occupation

Mar 2026: Brookings: radiology identified as theoretically well-suited for AI despite capability advances, actual adoption limited.

[Source: Brookings Institution]