Multimodal Medical Imaging
Documentsmultimodal-medical-imagingMultimodal Medical Imaging is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports.
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/ai-llm/multimodal-analysis-mdbabumiamssm-llms-universal-life/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/multimodal-medical-imaging, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
---name: multimodal-medical-imaging description: Analyzes medical images (X-ray, MRI, CT) using multimodal LLMs to identify anomalies and generate reports. license: MIT metadata: author: AI Group version: "1.0.0" compatibility:
- system: Python 3.10+ allowed-tools:
- run_shell_command
- read_file
keywords:
- multimodal-analysis
- automation
- biomedical measurable_outcome: execute task with >95% success rate. ---"
Multimodal Medical Imaging Analysis
The Multimodal Medical Imaging Analysis Skill leverages state-of-the-art Vision-Language Models (VLMs) like Gemini 1.5 Pro and GPT-4o to interpret medical imagery alongside clinical text.
When to Use This Skill
- When you need a preliminary screening of medical images.
- When correlating visual findings with textual clinical notes.
- To generate structured reports (DICOM-SR-like) from raw images.
Core Capabilities
- Anomaly Detection: Identify potential pathologies in X-rays, CTs, etc.
- Report Generation: Draft radiology reports in standard formats.
- VQA (Visual Question Answering): Answer specific questions about an image (e.g., "Is there a fracture in the left femur?").
Workflow
- Input: Provide an image file path (JPG, PNG) and a specific clinical question or "generate report" instruction.
- Analyze: The agent sends the image and prompt to the VLM.
- Output: Returns a JSON object with findings, confidence scores, and reasoning.
Example Usage
User: "Analyze this chest X-ray for pneumonia."
Agent Action:
python3 Skills/Clinical/Medical_Imaging/Multimodal_Analysis/multimodal_agent.py \
--image "/path/to/cxr.jpg" \
--prompt "Check for signs of pneumonia and consolidation."