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Learner Reviews & Feedback for Evaluations of AI Applications in Healthcare by Stanford University

141 ratings

About the Course

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. The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content....

Top reviews


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


Mar 5, 2021

This is a holistic course giving all perspectives and knowledge on the different aspects of evaluating all kind of AI driven solutions in healthcare. A must do for all healthcare Managers.

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26 - 35 of 35 Reviews for Evaluations of AI Applications in Healthcare

By Daniela C

Dec 16, 2022

Great course!

By Dimosthenis D G

May 17, 2021

Great course!

By Dr V S

Aug 12, 2022


By Tajan K

Dec 31, 2020

This course contains an extraordinary amount of considerations and information critical to the process of development and deployment of AI Applications in Healthcare. The topics include Evaluation Frameworks, Deployment Methodologies, Regulatory considerations, key considerations of fairness and bias in AI Applications, and Ethical considerations in AI. In many ways, any organization that intends to or is in the process of developing AI Applications should take these topics into consideration even before development begins. The material provided includes references to helpful frameworks and guidelines, which if used in the initial stages of the life cycle of an AI Application, would probably help reduce lead time, as also enable the deployment usage of the application in actual healthcare settings.

By Benjamin E

Jul 24, 2021

An imporatnt topic area, but a little hard to digest as it uses a lot of terminology and frameworks which are not necessarily well defined. I think it would benefit from some simplification into principles and language that are more relatable, still cross referenced to the technical/regulator terminology.

By Natalia K

Aug 25, 2021

There are several mistakes in the final exam questions. Check back. In terms of meaning, you can guess about the correct answer, but you need to fix it.

By Pierre J

Jul 10, 2022

Useful content, but there is a lot of repetition early in the course.

By Richard J

Apr 7, 2021


By Yuanqin M

Jan 21, 2021

too many theories but no practica examples or exercises

By Francesca M B

Jan 8, 2022

Very US focused