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Using machine learning to support the clinical back end
AI/ML

Using machine learning to support the clinical back end

7:51

MatrixCare CTO John Weatherbie describes how his company is using AI to stay ahead of the curve.

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3:54

(Sponsored) How synthetic data could redesign medical device regulation

Stuart Harrison, cofounder and CEO of ETHOS, discusses how synthetic data and real-world evidence could support safer and more efficient validation of AI-enabled medical devices through the Horizon Europe project, INSAFEDARE.

(Sponsored) How synthetic data could redesign medical device regulation
Stuart Harrison, cofounder and CEO of ETHOS, discusses how synthetic data and real-world evidence could support safer and more efficient validation of AI-enabled medical devices through the Horizon Europe project, INSAFEDARE.
3:54
5:24

AMAM model provides a roadmap for analytics, AI adoption

Dr. Seyoung Jung, Seoul National University Bundang Hospital CIO, says that going through the HIMSS AMAM validation process helped hospital leaders understand how best to implement AI and analytics tools.

AMAM model provides a roadmap for analytics, AI adoption
Dr. Seyoung Jung, Seoul National University Bundang Hospital CIO, says that going through the HIMSS AMAM validation process helped hospital leaders understand how best to implement AI and analytics tools.
5:24
5:04

As AI evolves, it needs new oversight layers

Dr. Ryan Sadeghian, University of Toledo CMIO, says governance frameworks must be strengthened to keep up with AI technology's growth so that AI tools can be safely scaled across clinical and operational workflows.

As AI evolves, it needs new oversight layers
Dr. Ryan Sadeghian, University of Toledo CMIO, says governance frameworks must be strengthened to keep up with AI technology's growth so that AI tools can be safely scaled across clinical and operational workflows.
5:04