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multimodal-medical-imaging

Maintainer FreedomIntelligence · Last updated April 1, 2026

Multimodal medical imaging analysis: CT, MRI, PET fusion and segmentation.

OpenClawNanoClawAnalysisReproductionmultimodal-medical-imaging🧠 bioos extended suiteclinical ai & healthcaremultimodal

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/multimodal-medical-imaging

Skill Snapshot

2 min

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 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.
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.

Source Doc

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?").

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.