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مهن الرعاية الصحية والتقنيين · المهارات والكفاءات الوظيفية · SOC 29-1224 · 147 مهارة

سياق المهنة

تصف مهام O*NET هذه المهنة وتوضح سياق تصنيف SOC، لكنها لا تعني أن كل مهارة مدرجة تنفذ كل مهمة.

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.

تُعرض بيانات مهام O*NET بلغتها المصدرية (الإنجليزية). بيانات المهام مقتبسة من O*NET® 29.1 Database التابعة لـ USDOL/ETA، وتُستخدم بموجب CC BY 4.0.

مصادر تمثيلية

عينة محدودة من المهارات الشائعة المرتبطة بهذه المهنة، بمهارة واحدة على الأكثر من كل مستودع GitHub. وتليها القائمة الكاملة للمهارات المرتبطة.

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.

كل المهارات المرتبطة

تصفح دليل الارتباط الكامل حسب شعبية المستودع أو أحدث نشاط في المصدر.

تبقى أسماء المهارات وأوصافها باللغة التي يوفرها مستودع المصدر.

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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.

imaging-study-design

awslabs/hcls-agent-skills

Reasoning skill for medical imaging study design and biomarker selection. Use when the user asks to plan an imaging study, choose a preprocessing strategy, select an imaging biomarker, design a radiomics pipeline, handle DICOM de-identification, plan longitudinal imaging analysis, or pick a registration target. Triggers include "imaging study", "preprocessing strategy", "DICOM de-identification", "imaging biomarker", "radiomics", "longitudinal imaging", "registration target", "MNI vs native space", "scanner harmonization", "ComBat", "IBSI", "multi-site imaging", "burned-in PHI", "test-retest reliability", "volumetric biomarker", "diffusion MRI", "perfusion imaging", "fMRI study design", "spectroscopy biomarker".

radiology-preprocessing

awslabs/hcls-agent-skills

Structural MRI/CT preprocessing pipeline for radiology workflows covering skull stripping, bias field correction, registration, and intensity normalization. Triggers on skull stripping, bias correction, registration, ANTs, FSL, HD-BET, N4, brain extraction, normalization, FLIRT, FNIRT, SyN, fslreorient2std, MNI registration, T1 preprocessing.

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.