Videos and Podcasts
Why AI in Healthcare Does Not Have to be a “Black Box”
It’s not enough for predictive models to be accurate, they must also be explainable. One of the main barriers to adopting artificial intelligence for hospitals and clinicians is their concern it’s a “black box” making it difficult to trust the results.
Andrew Eye joins the DataPoint podcast and explains how ClosedLoop unpacks AI’s “black box” by allowing data scientists and clinicians to understand why and how factors impact a model’s prediction, driving faster adoptions and better clinical results.
Listen to the podcast
Related Resources
How and Why You Should Assess Bias & Fairness in Healthcare AI Before Deploying to Clinical Workflows
Watch the on-demand session to learn why it's important to evaluate algorithms for bias before deployment and what metrics you can use to assess bias. Plus, get a demo of new product features built precisely for this purpose.
Algorithmic Bias in Healthcare AI and How to Avoid It
In this AIMed webinar, ClosedLoop explains why assessing algorithmic bias is critical for ensuring healthcare resources are fairly allocated to members. Tune in as AIMed hosts an important conversation about avoiding hidden bias in healthcare AI.
Explainable AI for Health
Dave DeCaprio, Co-Founder and CTO at ClosedLoop, sits down with Ian Alrahwan of the The University of Texas at Austin AI Heath Lab.
