structuring-radiology-reports

Convert free-text radiology reports into structured findings, impressions, measurements, and follow-up recommendations.

5.0k|615|Updated Oct 4, 2025

Search Tags:#medical imaging #structured findings #clinical NLP #follow-up tracking #radiology reports #BI-RADS #Lung-RADS

Ready to run

npx skills add https://github.com/maziyarpanahi/openmed --skill structuring-radiology-reports

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: structuring-radiology-reports
Source: https://github.com/maziyarpanahi/openmed/tree/main/skills/structuring-radiology-reports
Command: npx skills add https://github.com/maziyarpanahi/openmed --skill structuring-radiology-reports

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill converts free-text radiology reports into organized, reviewable clinical structures without losing measurements, laterality, assessment categories, or follow-up recommendations.

Core Features & Use Cases

Section Structuring: Separates technique, comparison, findings, and impression sections, including reports with inconsistent formatting.
Finding Extraction: Captures anatomy, disease or finding, measurements, laterality, temporal change, source offsets, and incidental findings.
Assessment and Follow-up Tracking: Extracts BI-RADS and Lung-RADS categories, recommended modalities, and follow-up intervals for downstream review.
Use Case: Structure a chest CT report containing an enlarging pulmonary nodule, preserve its dimensions and right-sided location, capture the Lung-RADS category, and record the recommended imaging follow-up.

Quick Start

Use the structuring-radiology-reports skill to de-identify the attached radiology report, extract its clinical entities, and return structured findings with measurements, laterality, assessment, and follow-up recommendations.

Frequently Asked Questions about structuring-radiology-reports

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract structured findings and BI-RADS categories from free-text radiology reports?▼

To extract structured findings and BI-RADS categories from free-text radiology reports, use a skill that performs section-aware parsing and clinical entity extraction. It de-identifies the text, segments sections, and captures anatomy, laterality, measurements, assessment categories, and follow-up recommendations.

Can I parse CT and MRI narratives to preserve lesion measurements and temporal changes?▼

Yes, you can parse CT and MRI narratives to preserve lesion measurements and temporal changes. The processing logic captures dimensions, laterality, and temporal-change documentation by applying source offsets and OpenMed clinical entity extraction to the radiology report text.

What is the best way to track incidental findings and Lung-RADS recommendations in chest CT reports?▼

The best way to track incidental findings and Lung-RADS recommendations is to use an extraction tool that identifies assessment categories and recommended follow-up intervals. This approach specifically captures Lung-RADS scores, recommended modalities, and incidental findings for downstream review.

Does this radiology report structuring approach handle inconsistent formatting across ultrasound and X-ray documents?▼

Yes, this radiology report structuring approach handles inconsistent formatting across ultrasound and X-ray documents. It separates technique, comparison, findings, and impression sections regardless of formatting inconsistencies, binding laterality and measurements to the correct clinical entities.

How do I prepare RadLex or DICOM-SR compliant data from unstructured medical imaging reports?▼

To prepare RadLex or DICOM-SR compliant data from unstructured medical imaging reports, apply clinical NLP to de-identify the text and extract structured findings. This process yields organized clinical structures with source offsets, measurements, and assessment categories ready for compliance mapping.

What are the limitations of automated clinical NLP for radiology report structuring regarding negation and uncertainty?▼

A key limitation of automated clinical NLP for radiology report structuring is handling negation, uncertainty, and decision-support safety. The extracted structured findings, impressions, and follow-up recommendations require clinician review to ensure the accuracy of negated symptoms and uncertain diagnoses.