This course is part of AI in Healthcare Specialization
Instructors: Tina Hernandez-Boussard
18,882 already enrolled
(278 reviews)
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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Readings related to best ethical practices for AI in health care
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Stanford University
Stanford University
DeepLearning.AI
DeepLearning.AI
278 reviews
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Reviewed on Apr 16, 2023
It is an excellent course on how we should evaluate AI solutions.
Reviewed on Apr 7, 2022
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
Reviewed on Oct 16, 2020
Nicely Framed and Executed in a simple language so anyone can catch up earliest.
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Dates and Duration
Original Release Date: 08/10/2023
Expiration Date: 08/10/2026
Estimated Time to Complete: 9 hours and 30 minutes
CME Credits Offered: 9.50
Accreditation
The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The Stanford University School of Medicine designates this enduring material for a maximum of 9.50 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Disclosures
The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. There are no relevant financial relationships with ACCME-defined commercial interests for anyone who was in control of the content of this activity.
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