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Lunit invites you to their event

From retrospective to prospective trials of AI in breast cancer screening

About this event

📢 Though the live webinar is over, you can still watch it on demand. Register now, and you will receive a confirmation email with link to access our on-demand webinar.
📢 For further questions or a demo request, please click here.
📢 After you've watched it, visit our online demo website, insight.lunit.io.



Dr. Fredrik Strand, MD PhD, from the Karolinska University Hospital in Sweden, will be presenting his studies featuring Lunit INSIGHT MMG, an AI solution for mammography.

He is going to go over his retrospective study published in JAMA Oncology in 2020 where he performed an external evaluation of three commercially available artificial intelligence algorithms as independent mammography readers.

Then he will be introducing his prospective clinical trial in a true screening population that had begun recently in March 2021, to further examine to what extent AI can replace or assist radiologists in breast cancer screening.

In this webinar, you will be able to learn:

  • the overall performance of Lunit INSIGHT MMG in breast cancer detection, compared to other commercially available AI solutions as well as radiologists
  • the adoption of AI combined with radiologists in clinical settings and routine practice
  • the aim and design of a prospective clinical trial within full population-based study populations

Hosted by

  • Team member
    T
    Steve Slasinski VP of Sales, North America @ Lunit, Inc.

    Performance driven, assertive Sales Executive, with a track record of driving operational initiatives that propel revenue, growth, expand market share and increase competitive advantages for start up and international companies, such as: R2 Technology, TeraRecon, ScreenPoint Medical & GEHC.

Lunit

AI will be the new standard of care. By Lunit.

Lunit, abbreviated from “learning unit,” is an AI software company devoted to developing advanced medical image analytics and data-driven imaging biomarkers via cutting-edge deep learning technology.