Integrating AI Into Research, Processes, and Products Involving Medical Imaging

This is an intensive course intended to give clinicians, biomedical researchers, and medical industry professionals practical knowledge to help them determine how to incorporate medical image AI into their practice, research, processes, and products.   Special emphasis is placed on MONAI, the freely available, open-source platform for medical image AI research and product development.   Software coding skills are not required, but we will be reviewing the software structure of AI training systems as well as exploring no-code solutions.

Target Audience

 Including but not limited to:

  • Medical and public health professionals, faculty, and students
  • Biomedical engineering and biological science professionals, faculty, and students
  • Entrepreneurs, established business leaders, and project managers wanting to incorporate medical images into their products and processes


  • Strengths and weaknesses of the state of the art in medical image AI
  • The role of MONAI, public data collections, and cloud computing in medical image AI R&D
  • Open-source tools for annotating medical images and training AI systems, without coding
  • Methods for understanding the basis of AI decisions, i.e., explainable AI
  • Options for deploying medical image AI systems into clinical workflows


  • Experience with medical images and their role in clinical decisions.
  • Familiarity with image processing software. For example, experience using clinical workstations or related software such as Osirix or 3D Slicer, or experience using photo editing applications such as Adobe Photoshop or related applications.
  • Python script writing experience is a bonus. It is not required, with the majority of the course focusing on existing tools rather than coding.


This course is approximately 8 hours, which can be divided into 2 sessions.

Introduction and overview (15 min)

Use cases of MONAI for medical image AI (20 min)

End-to-end medical image AI

  • MONAI Overview (30 min)
    • The open-source MONAI ecosystem
      • MONAI Core, Label, Deploy, and Zoo
    • Image segmentation, classification, and generation
    • Explainable AI
  • Data challenges and opportunities (20 min)
    • NIH The Cancer Imaging Archive and Imaging Data Commons
    • Licensing
    • FDA and DEI
  • Cloud Computing for Medical Image AI (20 min)
    • Amazon Studio and Studio Lab
    • Amazon Health Lake Imaging
  • No-Code AI: AI-assisted annotation and training (1 hr)
    • 3D Slicer + MONAI Label

MONAI Introduction Recap

MONAI Development tools and tips

  • Research environments (1 hr)
    • Jupyter Notebooks running MONAI in the cloud
    • Visualization for clinical collaboration
  • AI-assisted annotation API (30 min)
    • MONAI Label
  • Prototyping medical image applications (1 hr)
    • First-in-human studies
    • 3D Slicer and trame
  • Deploying into clinical workflows (1 hr)
    • MONAI Deploy

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