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
Evaluations of AI Applications in Healthcare
This course is part of AI in Healthcare Specialization
Instructors: Tina Hernandez-Boussard +1 more
24,954 already enrolled
Gain insight into a topic and learn the fundamentals.
(330 reviews)
Beginner level
No prior experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
94%
Most learners liked this course
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
Skills you'll gain
- Category: Predictive Modeling
- Category: Data Ethics
- Category: Health Technology
- Category: Regulatory Compliance
- Category: Continuous Monitoring
- Category: Healthcare Industry Knowledge
- Category: Application Deployment
- Category: Clinical Assessment
- Category: Decision Support Systems
- Category: Health Equity
- Category: Model Evaluation
- Category: Responsible AI
- Category: Health Informatics
- Category: Healthcare Ethics
- Category: Medical Devices
- Category: Artificial Intelligence
Details to know
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Assessments
22 assignments
Taught in English
Build your subject-matter expertise
This course is part of the AI in Healthcare Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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
What's included
19 videos2 readings5 assignments
What's included
18 videos2 readings5 assignments
What's included
18 videos2 readings4 assignments
Readings related to best ethical practices for AI in health care
What's included
3 readings
What's included
9 videos
What's included
3 readings1 assignment
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Instructors
Instructor ratings
(100 ratings)Tina Hernandez-Boussard
Stanford University
2 Courses 32,252 learners
Mildred Cho
Stanford University
2 Courses 98,300 learners
Offered by
Stanford University
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Showing 3 of 330
GL
Reviewed on 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.
KN
Reviewed on Dec 2, 2020
More examples would have been better to understand some of the concepts.
KS
Reviewed on Oct 16, 2020
Nicely Framed and Executed in a simple language so anyone can catch up earliest.
Frequently asked questions
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.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.