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OpenClaw-Medical-Skills radgpt-radiology-reporter

<!--

install
source · Clone the upstream repo
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/radgpt-radiology-reporter" ~/.claude/skills/freedomintelligence-openclaw-medical-skills-radgpt-radiology-reporter && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/radgpt-radiology-reporter" ~/.openclaw/skills/freedomintelligence-openclaw-medical-skills-radgpt-radiology-reporter && rm -rf "$T"
manifest: skills/radgpt-radiology-reporter/SKILL.md
tags
#radiology#report-generation#patient-friendly#summarization#explanation
source content
<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

name: radgpt-radiology-reporter description: Radiology Reporter keywords:

  • radiology
  • report-generation
  • patient-friendly
  • summarization
  • explanation measurable_outcome: Generate a patient-friendly explanation of a radiology report with <1% hallucination rate within 30 seconds. license: MIT metadata: author: Stanford Medicine version: "1.0.0" compatibility:
  • system: Python 3.9+ allowed-tools:
  • run_shell_command
  • read_file

RadGPT (Radiology Report Assistant)

An LLM-based agent designed to summarize and explain complex radiology reports for patients and clinicians.

When to Use

  • Patient Communication: Converting technical findings into plain language.
  • Clinician Review: Highlighting critical findings (e.g., "Pneumothorax detected").
  • Follow-up: Suggesting appropriate next steps based on findings.

Core Capabilities

  1. Simplification: Translates "bilateral opacity" to "cloudiness in both lungs".
  2. Entity Extraction: Identifies key anatomical structures and pathologies.
  3. Q&A: Answers follow-up questions about the report.

Workflow

  1. Input: Raw text of the radiology report.
  2. Process: LLM summarizes and identifies key findings.
  3. Output: Structured summary or conversational explanation.

Example Usage

User: "Explain this chest X-ray report to the patient."

Agent Action:

python -m radgpt.explain --report ./report.txt --target_audience patient
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->