The MONAI Deploy Application SDK offers a framework and associated tools to design, verify and analyze the performance of AI-driven applications in the healthcare domain.

It contains the following elements:

  • Pythonic framework for app development

  • A mechanism to package an app in a “MONAI Application Package” (MAP) instance

  • A mechanism to locally run a MAP via App Runner

  • A set of sample applications

  • API documentation

To further accelerate the development of medical imaging AI inference application with DICOM imaging network integration, a set of domain specific functionalities are provided by this Application SDK,

  • generic application classes to automate the inference with MONAI Bundle

  • DICOM study parsing and selection classes, as well as DICOM instance to volume image conversion

  • DICOM instance writers to encapsulate AI inference results in these DICOM OID,

    • DICOM Segmentation

    • DICOM Basic Text Structured Report

    • DICOM Encapsulated PDF

    • more are coming


  • The software and the imaging AI results generated are for research use only, and per applicable laws and regulations, are not for clinical use.

  • The App SDK, and specifically the DICOM instance writers, rely upon the underlying operating system to provide accurate and consistent time, though have no direct control over how the OS performs time synchronization, e.g. NTP on Ubuntu. For creation of DICOM or HL7 objects that use time, the built-in classes do, and it would also be incumbent on the developer to appropriately use the available system time service and reflect the correct time in those objects properly. Additionally, the DICOM instance generated by the App SDK built-in classes set the Timezone Offset From UTC with the underlying operating system’s timezone setting.