Displays DICOM medical images interactively within large language model interfaces like Claude Desktop.
Sponsored
Integrate medical imaging directly into your LLM workflows with this prototype MCP App. It processes DICOM files server-side, converting complex pixel data into viewable images and embedding them directly into LLM interfaces. This approach bypasses common Content Security Policy restrictions, enabling seamless series navigation, pan, zoom, and display of critical image metadata within your AI assistant environment.
Key Features
01Pan and zoom controls for detailed image inspection
021 GitHub stars
03Displays image metadata (dimensions, bit depth, instance number)
04Server-side rendering handles DICOM window/level and rescale slope/intercept
05View DICOM medical images directly within Claude Desktop
06Series navigation via slider, buttons, scroll wheel, and keyboard shortcuts
Use Cases
01Providing visual context for discussions and analysis of DICOM data within an LLM interface
02Enabling interactive visualization of medical studies during AI-assisted consultations
03Integrating medical image review into LLM-driven diagnostic support systems
