structuring-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.

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.

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.

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.

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.

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.