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Clinical Validation

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We are conducting prospective, real-time clinical validation studies of artificial intelligence models for medical imaging.

Research Questions

  • How does an AI model influence the performance of a radiologist?
  • How well does an AI model developed at one institution perform at another institution?

In the past, lack of due diligence in answering these questions stalled the adoption of computer-aided detection (CADe) algorithms for mammography. We want to prevent the same fate for AI in medical imaging by setting a higher standard of quality for AI models intended for use in clinical practice.

We are conducting prospective, real-time randomized controlled trials at multiple institutions to answer these questions. Conducting trials across multiple institutions affords a more robust clinical validation study across different patient populations and imaging devices.

We are starting with a Bone Age model; we will soon be adding new models for chest x-rays, head-CT and more.

Study Participation

We deeply integrate AI models into the clinical workflow to make participation in randomized controlled trials as easy as possible.

If you’re a radiologist ...

  • Results from the AI model are directly filled into your report and integrated with your PACS viewer.


If you’re an AI researcher ...

  • Model design and training are separated from clinical evaluation and use. After you train your deep learning model, you can initiate a validation study by uploading your model definition files and weights.

Let’s collaborate!

We are looking for new collaborations with hospital partners and AI researchers! Please reach out to if you’re interested!