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OsiriXGPT: An Innovative AI Co-pilot Plug-In for Seamless Deployment of Generative AI Models in Scan-to-Scan

Antonio Candito1,2, Timothy Sum-Hon Mun1, Richard Holbrey1

  • 1Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK.

Journal of Imaging Informatics in Medicine
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

OsiriXgrpc is an open-source API plugin enabling Generative Artificial Intelligence (GenAI) integration with DICOM viewers like OsiriX. This facilitates AI-driven radiology workflows and enhances diagnostic capabilities.

Keywords:
Large Language Models (LLMs)One-click AI-driven segmentation toolsOsiriXOsiriXgrpcRadiology reportingRadiology workflowVision-Language Models (VLMs)

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Radiology Workflow Optimization
  • Open-Source Software Development

Background:

  • Generative Artificial Intelligence (GenAI) offers transformative potential in radiology, including reduced reporting burdens and enhanced diagnostic workflows.
  • Limited research and adoption of GenAI in radiology stem from a lack of seamless integration with existing medical imaging viewers.
  • Bridging this gap is crucial for unlocking the full benefits of AI in clinical practice.

Purpose of the Study:

  • To introduce OsiriXgrpc, an open-source API plug-in designed to enable real-time communication between the OsiriX DICOM viewer and AI-driven tools.
  • To demonstrate the capability of OsiriXgrpc in facilitating the integration and visualization of AI-generated outputs within a clinical radiology setting.
  • To showcase the development of an AI Co-pilot for radiology leveraging OsiriXgrpc for iterative user-AI interactions.

Main Methods:

  • Development of OsiriXgrpc, an open-source API plug-in for OsiriX, a CE-marked and FDA-approved DICOM viewer.
  • Integration of GenAI models, including Large-Language Models (LLMs), Vision-Language Models (VLMs), and segmentation models (e.g., Segment Anything Model - SAM).
  • Implementation of an AI Co-pilot for radiology enabling "request-to-answer" interactions and real-time visualization of AI outputs within OsiriX.

Main Results:

  • Successful implementation of OsiriXgrpc, demonstrating seamless real-time communication between OsiriX and various GenAI models.
  • Visualization of AI-generated text outputs from LLMs and VLMs, and Regions of Interest (ROIs) generated by SAM within the OsiriX viewer.
  • Proof-of-concept validation of OsiriXgrpc's ability to support diverse AI tasks, including text generation and image segmentation.

Conclusions:

  • OsiriXgrpc effectively bridges the integration gap between DICOM viewers and GenAI tools, paving the way for advanced AI applications in radiology.
  • The developed AI Co-pilot demonstrates the practical utility of OsiriXgrpc for real-time AI interaction and output visualization in radiology.
  • OsiriXgrpc has the potential to lower adoption barriers for GenAI in clinical trials and routine healthcare, particularly in resource-limited settings.