放射線科医

医療従事者・技術職 · 職務スキル・コンピテンシー · SOC 29-1224 · 145 件の skills

職種の背景

以下の O*NET タスクは職種を説明し、SOC 分類の背景を示すものです。掲載されたすべての Skill が各タスクを実行できるという意味ではありません。

Prepare comprehensive interpretive reports of findings.
Perform or interpret the outcomes of diagnostic imaging procedures including magnetic resonance imaging (MRI), computer tomography (CT), positron emission tomography (PET), nuclear cardiology treadmill studies, mammography, or ultrasound.
Document the performance, interpretation, or outcomes of all procedures performed.

O*NET のタスク記述は原文（英語）で表示しています。タスクデータは USDOL/ETA の O*NET® 29.1 Database から抜粋し、CC BY 4.0 に基づき使用しています。

代表的なソース

この職種に分類された人気 Skill から抽出した限定サンプルです。GitHub リポジトリごとに最大1件を表示し、その下に完全な分類一覧を掲載します。

lamm-mit/scienceclaw

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

microsoft/healthcareai-examples

Ground CXR findings using the trained MI2 adapter. Returns per-finding positive/possible/negative calls for the agent to use when writing the report.

multimodal-medical-imaging

swaruplab/operon

Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports.

langcare-oncology

langcare/langcare-mcp-fhir

Compiles oncology treatment timeline from FHIR data including cancer staging (TNM), treatment history (chemo, radiation, surgery), lab trends (tumor markers, CBC), and response assessment (RECIST criteria). Use when asked about oncology timeline, cancer treatment history, tumor staging, treatment response, or cancer care summary.

分類されたすべての Skills

リポジトリの人気度またはソースの最新活動順で完全な分類一覧を閲覧できます。

Skill の名前と説明はソースリポジトリが提供する言語のまま表示されます。

並び替え

lamm-mit/scienceclaw

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

microsoft/healthcareai-examples

Ground CXR findings using the trained MI2 adapter. Returns per-finding positive/possible/negative calls for the agent to use when writing the report.

ground-zeroshot

microsoft/healthcareai-examples

Ground CXR findings using MI2 zero-shot classification. Returns per-finding probabilities bucketed into positive/possible for the agent to use when writing the report.

microsoft/healthcareai-examples

Entry point for the MedImageInsight plugin, covering the getting-started walkthrough, how the skills work, what MI2 is, and troubleshooting.

microsoft/healthcareai-examples

Read a chest X-ray end-to-end: ground findings with MedImageInsight, then write a structured radiologist report (Indication, Technique, Findings, Impression). Invoke for any "write/draft a CXR report" request.

multimodal-medical-imaging

swaruplab/operon

Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports.

langcare-oncology

langcare/langcare-mcp-fhir

Compiles oncology treatment timeline from FHIR data including cancer staging (TNM), treatment history (chemo, radiation, surgery), lab trends (tumor markers, CBC), and response assessment (RECIST criteria). Use when asked about oncology timeline, cancer treatment history, tumor staging, treatment response, or cancer care summary.

classifying-imaging-findings

CaseMark/skills

Applies standardized classification systems (BI-RADS, LI-RADS, TI-RADS, Lung-RADS, PI-RADS) to imaging findings. Use when categorizing imaging findings, applying RADS classifications, or determining follow-up recommendations.

creating-teaching-files

CaseMark/skills

Curates and annotates imaging cases for educational purposes with clinical correlation. Use when creating teaching files, annotating educational cases, or building case libraries.

documenting-critical-results

CaseMark/skills

Structures critical result communication with documentation requirements and closed-loop verification. Use when communicating critical findings, documenting urgent results, or verifying critical result acknowledgment.

documenting-tumor-board-presentations

CaseMark/skills

Structures multidisciplinary tumor board case presentations with radiology, pathology, and treatment synthesis. Use when preparing tumor board cases, presenting MDT discussions, or documenting consensus recommendations.

interpreting-musculoskeletal-imaging

CaseMark/skills

Structures MSK imaging interpretation with fracture classification and joint assessment protocols. Use when reading MSK imaging, classifying fractures, or documenting orthopedic findings.