Bioengineers and Biomedical Engineers
Architecture and Engineering Occupations- O*NET-SOC code
- 17-2031.00
Apply knowledge of engineering, biology, chemistry, computer science, and biomechanical principles to the design, development, and evaluation of biological, agricultural, and health systems and products, such as artificial organs, prostheses, instrumentation, medical information systems, and health management and care delivery systems.
AI exposure
- Data source: BLSPublished: 2026-08
Very 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
0.133
0.000Range of values carried here0.745Scale, 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
0.30
0.09Range of values carried here0.70Group-level value
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.
AI exposure (OpenAI rubric)
30 rated tasks · 29 tasks with β ≥ 0.5 (96.7%)
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) |
|---|---|---|
| Evaluate the safety, efficiency, and effectiveness of biomedical equipment. | Core | 0.5 |
| Advise hospital administrators on the planning, acquisition, and use of medical equipment. | Core | 0.5 |
| Research new materials to be used for products, such as implanted artificial organs. | Core | 0.5 |
| Develop models or computer simulations of human biobehavioral systems to obtain data for measuring or controlling life processes. | Core | 0.5 |
| Conduct research, along with life scientists, chemists, and medical scientists, on the engineering aspects of the biological systems of humans and animals. | Core | 0.5 |
| Adapt or design computer hardware or software for medical science uses. | Core | 1 |
| Conduct training or in-services to educate clinicians and other personnel on proper use of equipment. | Core | 0 |
| Write documents describing protocols, policies, standards for use, maintenance, and repair of medical equipment. | Core | 1 |
| Collaborate with manufacturing or quality assurance staff to prepare product specification or safety sheets, standard operating procedures, user manuals, or qualification and validation reports. | Core | 1 |
| Communicate with bioregulatory authorities regarding licensing or compliance responsibilities. | Core | 0.5 |
| Communicate with suppliers regarding the design or specifications of bioproduction equipment, instrumentation, or materials. | Core | 0.5 |
| Confer with research and biomanufacturing personnel to ensure the compatibility of design and production. | Core | 0.5 |
| Consult with chemists or biologists to develop or evaluate novel technologies. | Core | 0.5 |
| Design or conduct follow-up experimentation, based on generated data, to meet established process objectives. | Core | 0.5 |
| Design or develop medical diagnostic or clinical instrumentation, equipment, or procedures, using the principles of engineering and biobehavioral sciences. | Core | 1 |
| Develop methodologies for transferring procedures or biological processes from laboratories to commercial-scale manufacturing production. | Core | 0.5 |
| Develop statistical models or simulations, using statistical or modeling software. | Core | 1 |
| Maintain databases of experiment characteristics or results. | Core | 1 |
| Manage teams of engineers by creating schedules, tracking inventory, creating or using budgets, or overseeing contract obligations or deadlines. | Core | 0.5 |
| Prepare project plans for equipment or facility improvements, including time lines, budgetary estimates, or capital spending requests. | Core | 0.5 |
| Prepare technical reports, data summary documents, or research articles for scientific publication, regulatory submissions, or patent applications. | Core | 1 |
| Read current scientific or trade literature to stay abreast of scientific, industrial, or technological advances. | Core | 0.5 |
| Recommend process formulas, instrumentation, or equipment specifications, based on results of bench or pilot experimentation. | Core | 0.5 |
| Analyze new medical procedures to forecast likely outcomes. | Supplemental | 0.5 |
| Advise manufacturing staff regarding problems with fermentation, filtration, or other bioproduction processes. | Supplemental | 0.5 |
| Design and deliver technology, such as prosthetic devices, to assist people with disabilities. | Supplemental | 0.5 |
| Design or direct bench or pilot production experiments to determine the scale of production methods that optimize product yield and minimize production costs. | Supplemental | 0.5 |
| Develop bioremediation processes to reduce pollution, protect the environment, or treat waste products. | Supplemental | 0.5 |
| Lead studies to examine or recommend changes in process sequences or operation protocols. | Supplemental | 0.5 |
| Review existing manufacturing processes to identify opportunities for yield improvement or reduced process variation. | Supplemental | 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: 17-2031.00 Bioengineers and Biomedical Engineers
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.