Radiologistas

Profissionais e Técnicos de Saúde · Habilidades e competências profissionais · SOC 29-1224 · 145 skills

Contexto da ocupação

Estas tarefas do O*NET descrevem a ocupação e contextualizam o mapeamento SOC; não significam que cada Skill listado execute todas as tarefas.

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.

As tarefas do O*NET são exibidas no idioma de origem (inglês). Os dados de tarefas foram extraídos do O*NET® 29.1 Database da USDOL/ETA e são usados sob CC BY 4.0.

Fontes representativas

Uma amostra limitada de Skills populares mapeados para esta ocupação, com no máximo um Skill por repositório do GitHub. A lista completa aparece a seguir.

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.

Todos os Skills mapeados

Explore o mapeamento completo por popularidade do repositório ou atividade recente da fonte.

Os nomes e as descrições dos Skills permanecem no idioma fornecido pelo repositório de origem.

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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.

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.