Preventive Medicine Physicians
Healthcare Practitioners and Technical Occupations- O*NET-SOC code
- 29-1229.05
Apply knowledge of general preventive medicine and public health issues to promote health care to groups or individuals, and aid in the prevention or reduction of risk of disease, injury, disability, or death. May practice population-based medicine or diagnose and treat patients in the context of clinical health promotion and disease prevention.
AI exposure
- Data source: BLSPublished: 2026-08
High· relative
LowFour relative bandsVery highGroup-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
- Data source: AnthropicPublished: 2026-03
0.030
0.000Range of values carried here0.745Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
- Data source: ILOPublished: 2025
0.27–0.29· 2 ISCO-08 groups
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
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.
ILO value for each linked ISCO-08 group
- ISCO-08 2211Generalist Medical Practitioners0.29
- ISCO-08 2212Specialist Medical Practitioners0.27
AI exposure (OpenAI rubric)
15 rated tasks · 14 tasks with β ≥ 0.5 (93.3%)
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
- Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
- All rated tasks are in the O*NET 31.0 task list.
- Source
- OpenAI "GPTs are GPTs" exposure rubric
- Release
- gh-main-0471612
- License
- MIT License, Copyright (c) 2024 OpenAI
Tasks
Task statements from the O*NET® 31.0 Database, core tasks first.
| Task | Type | β (OpenAI) |
|---|---|---|
| Teach or train medical staff regarding preventive medicine issues. | Core | 0.5 |
| Document or review comprehensive patients' histories with an emphasis on occupation or environmental risks. | Core | 0.5 |
| Prepare preventive health reports, including problem descriptions, analyses, alternative solutions, and recommendations. | Core | 0.5 |
| Supervise or coordinate the work of physicians, nurses, statisticians, or other professional staff members. | Core | 0 |
| Deliver presentations to lay or professional audiences. | Core | 0.5 |
| Evaluate the effectiveness of prescribed risk reduction measures or other interventions. | Core | 0.5 |
| Identify groups at risk for specific preventable diseases or injuries. | Core | 0.5 |
| Design or use surveillance tools, such as screening, lab reports, and vital records, to identify health risks. | Core | 0.5 |
| Direct public health education programs dealing with topics such as preventable diseases, injuries, nutrition, food service sanitation, water supply safety, sewage and waste disposal, insect control, and immunizations. | Core | 0.5 |
| Perform epidemiological investigations of acute and chronic diseases. | Core | 0.5 |
| Develop or implement interventions to address behavioral causes of diseases. | Core | 0.5 |
| Direct or manage prevention programs in specialty areas such as aerospace, occupational, infectious disease, and environmental medicine. | Core | 0.5 |
| Design, implement, or evaluate health service delivery systems to improve the health of targeted populations. | Core | 0.5 |
| Coordinate or integrate the resources of health care institutions, social service agencies, public safety workers, or other organizations to improve community health. | Core | 0.5 |
| Provide information about potential health hazards and possible interventions to the media, the public, other health care professionals, or local, state, and federal health authorities. | Core | 0.5 |
Occupation information
Sources and attribution
This page includes information from the O*NET® 31.0 Database (https://www.onetcenter.org/database.html) by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O*NET® is a trademark of USDOL/ETA. AI Changing Work has modified all or some of this information: the O*NET-SOC code, title and task statements are reproduced in English without change; task-type labels are shown in the page's language and tasks are listed core first; any Korean occupation title shown on the Korean-language page is AI Changing Work's translation; any KSCO-8 unit groups linked to this occupation were paired with it by AI Changing Work's judgment, and the relation labels and statuses are AI Changing Work's additions. USDOL/ETA has not approved, endorsed, or tested these modifications.
Any AI exposure figures on this page are published by third parties, not by AI Changing Work, and none is part of the O*NET information. OpenAI publishes task-level scores (MIT License) for O*NET 27.2 task statements; each is shown next to the O*NET 31.0 task statement with the same task ID, whose wording can differ from the 27.2 statement that was scored. OpenAI also publishes occupation-level scores for O*NET-SOC codes in the same release, and any such score is shown on the O*NET occupation with the same code. Anthropic publishes an observed exposure index in the Anthropic Economic Index (CC-BY), and the U.S. Bureau of Labor Statistics publishes relative AI exposure categories (public domain); both are published per SOC code, and each value is shown on every O*NET occupation with that code. The International Labour Organization publishes a generative AI exposure index in ILO Working Paper 140 (CC BY 4.0) for ISCO-08 unit groups; AI Changing Work links those groups to O*NET occupations by applying the U.S. Bureau of Labor Statistics ISCO-08 to 2010 SOC and 2010 SOC to 2018 SOC crosswalks as published, without case-by-case selection, and these crosswalks match many groups only in part. Where several unit groups are linked, each group's published value is listed, and any summary shows only the lowest and highest of those values with the number of groups; no exposure figure is averaged or recalculated. Any employment figures are published by the U.S. Bureau of Labor Statistics for the SOC group containing this occupation. Each source is credited where its figures are shown.
O*NET OnLine: 29-1229.05 Preventive Medicine Physicians
KSCO 코드·명칭: 한국표준직업분류(제8차 개정) — 통계청 고시 제2024-328호 (2024-07-01 고시, 2025-01-01 시행). 저작권법 제7조 제2호의 고시 항목이다. 명칭 표기(가운뎃점·띄어쓰기)는 해설서 2차 정오 반영판의 표기를 따랐으며, 고시 항목표와는 18개 명칭에서 가운뎃점 글리프나 띄어쓰기만 다르다. 통계청은 2025년 10월 국가데이터처로 개편되었다. 이 페이지의 KSCO 연결은 통계청·국가데이터처의 공식 연계표가 아니다.