multimodal-medical-imaging

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"]

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

1. Anomaly Detection: Identify potential pathologies in X-rays, CTs, etc.
2. Report Generation: Draft radiology reports in standard formats.
3. VQA (Visual Question Answering): Answer specific questions about an image (e.g., "Is there a fracture in the left femur?").

Workflow

1. Input: Provide an image file path (JPG, PNG) and a specific clinical question or "generate report" instruction.
2. Analyze: The agent sends the image and prompt to the VLM.
3. 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."