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.
This course is part of the AI in Healthcare Specialization
About this Course
What you will 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
Syllabus - What you will learn from this course
AI in Healthcare
Evaluations of AI in Healthcare
Downstream Evaluations of AI in Healthcare: Bias and Fairness
- 5 stars70.21%
- 4 stars18.43%
- 3 stars6.38%
- 2 stars2.83%
- 1 star2.12%
TOP REVIEWS FROM EVALUATIONS OF AI APPLICATIONS IN HEALTHCARE
Useful content, but there is a lot of repetition early in the course.
I was expecting the Medical genetics professor as a teacher also.
This course was really valuable for linking and embedding my knowledge gained by reading FDA guidance documents and knowledge sharing from my Quality Assurance and Regulatory Affairs colleagues
More examples would have been better to understand some of the concepts.
About the AI in Healthcare Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
Is this activity accredited for Continuing Medical Education (CME)?
More questions? Visit the Learner Help Center.