multimodal-analysis
Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports.
Radiology Reporter
--- 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 --- <!-- # 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 --> # 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**: ```bash python -m radgpt.explain --report ./report.txt --target_audience patient ``` <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->--- 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 --- <!-- # 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 --> # 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**: ```bash python -m radgpt.explain --report ./report.txt --target_audience patient ``` <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.