structuring-radiology-reports
Converts free-text radiology narratives into structured findings and impression — with measurements, laterality, anatomy, and follow-up recommendations — after OpenMed NER. Use when the user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique, comparison, findings, impression), lesion measurements and laterality captured, BI-RADS / Lung-RADS assessment categories pulled, or incidental findings and recommended follow-up tracked. Trigger keywords: radiology report, findings, impression, RadLex, DICOM-SR, BI-RADS, Lung-RADS, ACR, laterality, measurement, nodule, incidental finding, follow-up, structured reporting. Pairs after OpenMed: run openmed.analyze_text on the report (Anatomy/Disease/measurement entities), then assemble structured findings. De-identify the report first. Decision-support only — not a diagnostic medical device.
72
87%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The required workflow ingests the radiology report’s `deidentified_text` (openmed extracts and runs NER on it), and since that report is outsider-authored free text in typical use (not authored by the operating user), the LLM context can be fed that untrusted prose via `openmed.analyze_text(text, ...)` on runtime text.
- Repository
- maziyarpanahi/openmed
- Commit
80da98c
- Audited
- Security analysis
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.