With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.
Evaluations of AI Applications in Healthcare
This course is part of AI in Healthcare Specialization
Instructors: Tina Hernandez-Boussard
Sponsored by University of Michigan
16,500 already enrolled
(256 reviews)
What you'll learn
Principles and practical considerations for integrating AI into clinical workflows
Best practices of AI applications to promote fair and equitable healthcare solutions
Challenges of regulation of AI applications and which components of a model can be regulated
What standard evaluation metrics do and do not provide
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There are 8 modules in this course
What's included
10 videos2 readings3 assignments
What's included
15 videos2 readings4 assignments
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19 videos2 readings5 assignments
What's included
18 videos2 readings5 assignments
What's included
18 videos2 readings4 assignments
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Reviewed on Jul 9, 2022
Useful content, but there is a lot of repetition early in the course.
Reviewed on Oct 16, 2020
Nicely Framed and Executed in a simple language so anyone can catch up earliest.
Reviewed on Dec 12, 2020
Thanks a lot for this great course, extremely practical, allowing to create a clear action plan for AI evaluation.
Recommended if you're interested in Data Science
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