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There are 8 modules in this 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.
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
What's included
10 videos2 readings3 assignments
Show info about module content
10 videos•Total 30 minutes
Learning Objectives•1 minute
Common Definitions•2 minutes
Overview•1 minute
Why AI is needed in Healthcare•5 minutes
Examples of AI in Healthcare•8 minutes
Growth of AI in Healthcare•3 minutes
Questions Answered by AI•2 minutes
AI Output•4 minutes
Think beyond area under the curve•1 minute
Recap•2 minutes
2 readings
Study Guide Module 1•0 minutes
Citations and Additional Readings•0 minutes
3 assignments•Total 40 minutes
Reflection Exercise 1•10 minutes
Reflection Exercise 2•10 minutes
Knowledge Check•20 minutes
Evaluations of AI in Healthcare
Module 2•2 hours to complete
Module details
What's included
15 videos2 readings4 assignments
Show info about module content
15 videos•Total 41 minutes
Learning Objectives•1 minute
Recap: Framework•1 minute
Stakeholders•2 minutes
Clinical Utility•2 minutes
Outcome: Action Pairing, An Overview•4 minutes
Lead Time•1 minute
Type of Action•4 minutes
OAP Examples•4 minutes
Number Needed to Treat•3 minutes
Net Benefits•3 minutes
Decision Curves•4 minutes
Feasibility overview•5 minutes
Implementation Costs•2 minutes
Clinical Evaluation and Uptake•5 minutes
Summary•2 minutes
2 readings•Total 5 minutes
Study Guide Module 2•5 minutes
Citations and Additional Readings•0 minutes
4 assignments•Total 60 minutes
Reflection Exercise 1•10 minutes
Reflection Exercise 2•10 minutes
Reflection Exercise 3•10 minutes
Knowledge Check•30 minutes
AI Deployment
Module 3•2 hours to complete
Module details
What's included
19 videos2 readings5 assignments
Show info about module content
19 videos•Total 47 minutes
Learning Objectives•2 minutes
The Problem•2 minutes
Practical Questions Prior to Deployment•4 minutes
Deployment Pathway•2 minutes
Design and Development•3 minutes
Stakeholder Involvement•2 minutes
Data Type and Sources•3 minutes
Settings•3 minutes
In Silico Evaluation•2 minutes
Net Utility & Work Capacity•2 minutes
Statistical Validity•1 minute
Care Integration, Silent Mode•3 minutes
Clinical Integration, Considerations•3 minutes
Technical Integration•1 minute
Deployment Modalities•3 minutes
Continuous Monitoring and Maintenance•2 minutes
Challenges of Deployment•5 minutes
Sepsis Example•2 minutes
Summary•2 minutes
2 readings•Total 5 minutes
Study Guide Module 3•5 minutes
Citations and Additional Readings•0 minutes
5 assignments•Total 70 minutes
Reflection Exercise 1•10 minutes
Reflection Exercise 2•10 minutes
Reflection Exercise 3•10 minutes
Reflection Exercise 4•10 minutes
Knowledge Check•30 minutes
Downstream Evaluations of AI in Healthcare: Bias and Fairness
Module 4•2 hours to complete
Module details
What's included
18 videos2 readings5 assignments
Show info about module content
18 videos•Total 41 minutes
Learning Objectives•3 minutes
Real World Examples of AI Bias•5 minutes
Introduction - Types of Bias•1 minute
Historical Bias•2 minutes
Representation Bias•2 minutes
Measurement Bias•2 minutes
Aggregation Bias•2 minutes
Evaluation Bias•2 minutes
Deployment Bias•1 minute
What is algorithmic Fairness•2 minutes
Anti-classification•2 minutes
Parity Classification•2 minutes
Calibration•3 minutes
Applying Fairness Measures•3 minutes
Lack of Transparency•2 minutes
Minimal Reporting Standards•3 minutes
Opportunities and Challenges•3 minutes
Summary•3 minutes
2 readings•Total 5 minutes
Study Guide Module 4•5 minutes
Citations and Additional Readings•0 minutes
5 assignments•Total 70 minutes
Reflection Exercise 1•10 minutes
Reflection Exercise 2•10 minutes
Reflection Exercise 3•10 minutes
Reflection Exercise 4•10 minutes
Knowledge Check•30 minutes
The Regulatory Environment for AI in Healthcare
Module 5•2 hours to complete
Module details
What's included
18 videos2 readings4 assignments
Show info about module content
18 videos•Total 53 minutes
Learning Objectives•1 minute
The Problem•3 minutes
International Definitions Used for Regulatory Purposes•2 minutes
Definition Statement & Risk Framework•6 minutes
Valid Clinical Association•3 minutes
Analytical Evaluation •1 minute
Clinical Evaluation•3 minutes
General Control•2 minutes
de novo Notifications•2 minutes
Software Modification•3 minutes
TPLC•4 minutes
Locked vs Adapted AI solutions•2 minutes
Examples•4 minutes
Non-Regulated Products•2 minutes
EU Regulations•3 minutes
Chinese Guidelines•2 minutes
OMB Guidelines•6 minutes
Summary•4 minutes
2 readings•Total 5 minutes
Study Guide Module 5•5 minutes
Citations and Additional Readings•0 minutes
4 assignments•Total 60 minutes
Reflection Exercise 1•10 minutes
Reflection Exercise 2•10 minutes
Reflection Exercise 3•10 minutes
Knowledge Check•30 minutes
Best Ethical Practices for AI in Health Care
Module 6•1 hour to complete
Module details
Readings related to best ethical practices for AI in health care
What's included
3 readings
Show info about module content
3 readings•Total 30 minutes
Problem Formulation•10 minutes
Identifying Conflicts of Interest•10 minutes
Mitigating Conflicts of Interest•10 minutes
AI and Medicine (Optional Content)
Module 7•1 hour to complete
Module details
What's included
9 videos
Show info about module content
9 videos•Total 42 minutes
Introduction: Navigating the Intersections of AI and Medicine•3 minutes
Life Cycle of AI•9 minutes
A Deep Dive into Historical and Societal Dimensions•2 minutes
Race-Based Medicine and Race-Aware Approach•5 minutes
Bias Mitigation Strategies•5 minutes
Exploring Potentials and Ethical Quandaries•5 minutes
Dismantling Race-Based Medicine•4 minutes
Deploying AI into Healthcare Settings•7 minutes
Conclusion•1 minute
Course Wrap Up
Module 8•1 hour to complete
Module details
What's included
3 readings1 assignment
Show info about module content
3 readings•Total 25 minutes
Final Assessment Note•5 minutes
Claim CME Credit•10 minutes
Full Study Guide•10 minutes
1 assignment•Total 60 minutes
Final Exam•60 minutes
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The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
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Learner reviews
4.6
337 reviews
5 stars
73.59%
4 stars
15.43%
3 stars
6.23%
2 stars
2.67%
1 star
2.07%
Showing 3 of 337
G
GL
5·
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.
A
AH
5·
Reviewed on Dec 11, 2024
V easy to follow. Could finish at my own pace. Always theory went hand in hand with real life examples which made it interesting.
D
D
5·
Reviewed on Dec 7, 2020
I was expecting the Medical genetics professor as a teacher also.
Is this activity accredited for Continuing Medical Education (CME)?
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.
When will I have access to the lectures and assignments?
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.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.