Medical Dosimetrists
Healthcare Practitioners and Technical Occupations- رمز O*NET-SOC
- 29-2036.00
Generate radiation treatment plans, develop radiation dose calculations, communicate and supervise the treatment plan implementation, and consult with members of radiation oncology team.
تُعرض أسماء المهن وأوصاف المهام بالإنجليزية كما نُشرت. أما التسميات، ومنها أنواع المهام، فمترجمة.
التعرّض للذكاء الاصطناعي
- مصدر البيانات: BLSتاريخ النشر: 2026-08
مرتفع· نسبي
منخفضأربع فئات نسبيةمرتفع جدًاقيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
أربع فئات نسبية (منخفض / متوسط / مرتفع / مرتفع جدًا)
831 مهنة تفصيلية في جدول توقعات التوظيف لدى BLS. وتُسند القيمة على مستوى رمز مصفوفة التوظيف الوطنية (NEM)، فالمهن التي تشترك في الرمز نفسه تأخذ النطاق نفسه
- مصدر البيانات: ILOتاريخ النشر: 2025
0.30
0.09نطاق القيم المعروضة هنا0.70قيمة على مستوى المجموعة المهنية
المقياس والأساس والمصدر
مؤشر التعرّض للذكاء الاصطناعي التوليدي، 0–1 كما نُشر
قيمة منشورة على مستوى مجموعة الوحدة في ISCO-08. رُبطت بهذه المهنة بتطبيق جداول التناظر الرسمية لمكتب إحصاءات العمل الأمريكي (BLS) (من ISCO-08 إلى SOC 2010، ومن SOC 2010 إلى SOC 2018) كما نُشرت، والتناظر كلي أو جزئي
- مصدر البيانات: OpenAIتاريخ النشر: 2023
0.300
0.000نطاق القيم المعروضة هنا0.844المقياس والأساس والمصدر
ما نوع القيمة التي ينشرها هذا المصدر
فئة BLS رتبة نسبية لا مستوى مطلق، وليست قياسًا مباشرًا: فهي تجمع الرتب المئينية للمهنة في عدة دراسات منشورة في أربع فئات. وهي ليست توقّعًا للتوظيف أو الأجور، ولا احتمالًا للتبني، ولا تفرّق بين الأتمتة والتعزيز.
التعرض للذكاء الاصطناعي (معيار OpenAI)
20 مهمة مقيَّمة · 15 مهمة بقيمة β ≥ 0.5 (75.0%)
β = التعرّض المباشر (E1) + 0.5 × التعرّض عند توفر الأدوات (E2)، وفق تعريف المستودع المصدري.
- وحدة المصدر: مهام O*NET 27.2 ← رمز المهنة في O*NET 31.0
- عدد المهام المقيَّمة غير الموجودة في قائمة مهام O*NET 31.0: 1؛ وتبقى درجاتها كما نُشرت وفق O*NET 27.2.
- المصدر
- OpenAI "GPTs are GPTs" exposure rubric
- الإصدار
- gh-main-0471612
- الترخيص
- MIT License, Copyright (c) 2024 OpenAI
المهام
أوصاف المهام من O*NET® 31.0 Database، والمهام الأساسية أولًا.
| المهمة | النوع | β (OpenAI) |
|---|---|---|
| Advise oncology team members on use of beam modifying or immobilization devices in radiation treatment plans. | أساسية | 0.5 |
| Calculate, or verify calculations of, prescribed radiation doses. | أساسية | 1 |
| Calculate the delivery of radiation treatment, such as the amount or extent of radiation per session, based on the prescribed course of radiation therapy. | أساسية | 0.5 |
| Conduct radiation oncology-related research, such as improving computer treatment planning systems or developing new treatment devices. | أساسية | 0.5 |
| Create and transfer reference images and localization markers for treatment delivery, using image-guided radiation therapy. | أساسية | 0.5 |
| Design the arrangement of radiation fields to reduce exposure to critical patient structures, such as organs, using computers, manuals, and guides. | أساسية | 0.5 |
| Develop radiation treatment plans in consultation with members of the radiation oncology team. | أساسية | 0.5 |
| Develop requirements for the use of patient immobilization devices and positioning aides, such as molds or casts, as part of treatment plans to ensure accurate delivery of radiation and comfort of patient. | أساسية | 0.5 |
| Fabricate beam modifying devices, such as compensators, shields, and wedge filters. | أساسية | 0 |
| Fabricate patient immobilization devices, such as molds or casts, for radiation delivery. | أساسية | 0 |
| Identify and outline bodily structures, using imaging procedures, such as x-ray, magnetic resonance imaging, computed tomography, or positron emission tomography. | أساسية | 0.5 |
| Perform quality assurance system checks, such as calibrations, on treatment planning computers. | أساسية | 1 |
| Plan the use of beam modifying devices, such as compensators, shields, and wedge filters, to ensure safe and effective delivery of radiation treatment. | أساسية | 0.5 |
| Record patient information, such as radiation doses administered, in patient records. | أساسية | 1 |
| Supervise or perform simulations for tumor localizations, using imaging methods such as magnetic resonance imaging, computed tomography, or positron emission tomography scans. | أساسية | 0.5 |
| Teach medical dosimetry, including its application, to students, radiation therapists, or residents. | أساسية | 0 |
| Develop treatment plans, and calculate doses for brachytherapy procedures. | تكميلية | 0.5 |
| Educate patients regarding treatment plans, physiological reactions to treatment, or post-treatment care. | تكميلية | 0.5 |
| Measure the amount of radioactivity in patients or equipment, using radiation monitoring devices. | تكميلية | 0 |
معلومات مهنية
المصادر والإسناد
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: 29-2036.00 Medical Dosimetrists
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.