About this Course

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Beginner Level
Approx. 11 hours to complete
English

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

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level
Approx. 11 hours to complete
English

Offered by

Placeholder

Stanford University

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

AI in Healthcare

1 hour to complete
10 videos (Total 30 min), 2 readings, 3 quizzes
10 videos
Common Definitions1m
Overview1m
Why AI is needed in Healthcare4m
Examples of AI in Healthcare8m
Growth of AI in Healthcare2m
Questions Answered by AI2m
AI Output3m
Think beyond area under the curve1m
Recap2m
2 readings
Study Guide Module 1
Citations and Additional Readings
3 practice exercises
Reflection Exercise 110m
Reflection Exercise 210m
Knowledge Check20m
Week
2

Week 2

2 hours to complete

Evaluations of AI in Healthcare

2 hours to complete
15 videos (Total 41 min), 2 readings, 4 quizzes
15 videos
Recap: Framework1m
Stakeholders1m
Clinical Utility1m
Outcome: Action Pairing, An Overview4m
Lead Time1m
Type of Action3m
OAP Examples3m
Number Needed to Treat2m
Net Benefits2m
Decision Curves4m
Feasibility overview4m
Implementation Costs1m
Clinical Evaluation and Uptake4m
Summary1m
2 readings
Study Guide Module 25m
Citations and Additional Readings
4 practice exercises
Reflection Exercise 110m
Reflection Exercise 210m
Reflection Exercise 310m
Knowledge Check30m
Week
3

Week 3

2 hours to complete

AI Deployment

2 hours to complete
19 videos (Total 47 min), 2 readings, 5 quizzes
19 videos
The Problem1m
Practical Questions Prior to Deployment3m
Deployment Pathway1m
Design and Development3m
Stakeholder Involvement2m
Data Type and Sources3m
Settings3m
In Silico Evaluation2m
Net Utility & Work Capacity2m
Statistical Validity1m
Care Integration, Silent Mode2m
Clinical Integration, Considerations2m
Technical Integration1m
Deployment Modalities2m
Continuous Monitoring and Maintenance2m
Challenges of Deployment5m
Sepsis Example1m
Summary1m
2 readings
Study Guide Module 35m
Citations and Additional Readings
5 practice exercises
Reflection Exercise 110m
Reflection Exercise 210m
Reflection Exercise 310m
Reflection Exercise 410m
Knowledge Check30m
Week
4

Week 4

2 hours to complete

Downstream Evaluations of AI in Healthcare: Bias and Fairness

2 hours to complete
18 videos (Total 41 min), 2 readings, 5 quizzes
18 videos
Real World Examples of AI Bias4m
Introduction - Types of Bias41s
Historical Bias2m
Representation Bias1m
Measurement Bias1m
Aggregation Bias2m
Evaluation Bias1m
Deployment Bias1m
What is algorithmic Fairness2m
Anti-classification2m
Parity Classification1m
Calibration2m
Applying Fairness Measures2m
Lack of Transparency2m
Minimal Reporting Standards3m
Opportunities and Challenges2m
Summary2m
2 readings
Study Guide Module 45m
Citations and Additional Readings
5 practice exercises
Reflection Exercise 110m
Reflection Exercise 210m
Reflection Exercise 310m
Reflection Exercise 410m
Knowledge Check30m

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About the AI in Healthcare Specialization

AI in Healthcare

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