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Evaluations of AI Applications in Healthcare

Approx. 11 hours to complete

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Course Summary

This course explores the use of AI in healthcare evaluations and covers topics such as data collection, analysis, and ethical considerations.

Key Learning Points

Learn about the use of AI in healthcare evaluations
Discover methods for data collection and analysis
Understand the ethical considerations surrounding AI in healthcare

Learning Outcomes

Understand the use of AI in healthcare evaluations
Apply methods for data collection and analysis
Identify and address ethical considerations in AI healthcare research

Prerequisites or good to have knowledge before taking this course

Basic understanding of healthcare
Familiarity with data collection and analysis

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Quizzes
Assignments

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Description

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.

Knowledge

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

Outline

AI in Healthcare
Learning Objectives
Common Definitions
Overview
Why AI is needed in Healthcare
Examples of AI in Healthcare
Growth of AI in Healthcare
Questions Answered by AI
AI Output
Think beyond area under the curve
Recap
Reflection Exercise 1
Reflection Exercise 2
Knowledge Check

Evaluations of AI in Healthcare
Learning Objectives
Recap: Framework
Stakeholders
Clinical Utility
Outcome: Action Pairing, An Overview
Lead Time
Type of Action
OAP Examples
Number Needed to Treat
Net Benefits
Decision Curves
Feasibility overview
Implementation Costs
Clinical Evaluation and Uptake
Summary
Study Guide Module 2
Reflection Exercise 1
Reflection Exercise 2
Reflection Exercise 3
Knowledge Check

AI Deployment
Learning Objectives
The Problem
Practical Questions Prior to Deployment
Deployment Pathway
Design and Development
Stakeholder Involvement
Data Type and Sources
Settings
In Silico Evaluation
Net Utility & Work Capacity
Statistical Validity
Care Integration, Silent Mode
Clinical Integration, Considerations
Technical Integration
Deployment Modalities
Continuous Monitoring and Maintenance
Challenges of Deployment
Sepsis Example
Summary
Study Guide Module 3
Reflection Exercise 1
Reflection Exercise 2
Reflection Exercise 3
Reflection Exercise 4
Knowledge Check

Downstream Evaluations of AI in Healthcare: Bias and Fairness
Learning Objectives
Real World Examples of AI Bias
Introduction - Types of Bias
Historical Bias
Representation Bias
Measurement Bias
Aggregation Bias
Evaluation Bias
Deployment Bias
What is algorithmic Fairness
Anti-classification
Parity Classification
Calibration
Applying Fairness Measures
Lack of Transparency
Minimal Reporting Standards
Opportunities and Challenges
Summary
Study Guide Module 4
Reflection Exercise 1
Reflection Exercise 2
Reflection Exercise 3
Reflection Exercise 4
Knowledge Check

The Regulatory Environment for AI in Healthcare
Learning Objectives
The Problem
International Definitions Used for Regulatory Purposes
Definition Statement & Risk Framework
Valid Clinical Association
Analytical Evaluation
Clinical Evaluation
General Control
de novo Notifications
Software Modification
TPLC
Locked vs Adapted AI solutions
Examples
Non-Regulated Products
EU Regulations
Chinese Guidelines
OMB Guidelines
Summary
Study Guide Module 5
Reflection Exercise 1
Reflection Exercise 2
Reflection Exercise 3
Knowledge Check

Best Ethical Practices for AI in Health Care
Problem Formulation
Identifying Conflicts of Interest
Mitigating Conflicts of Interest

Course Wrap Up
Final Assessment Note
Claim CME Credit
Full Study Guide
Final Exam

Summary of User Reviews

Learn about the applications of AI in healthcare with Coursera's Evaluations of AI Applications in Healthcare course. Users have given this course positive reviews and praise its comprehensive approach to the subject matter.

Key Aspect Users Liked About This Course

Many users appreciated the course's practical approach to AI applications in healthcare.

Pros from User Reviews

Comprehensive coverage of AI applications in healthcare
Practical approach to learning with real-world case studies and examples
Engaging and knowledgeable instructors
Flexible schedule and self-paced learning
Great for healthcare professionals looking to expand their knowledge of AI

Cons from User Reviews

Some users felt that the course could benefit from more in-depth technical discussions
The course may not be suitable for those without a basic understanding of healthcare concepts
Some users found the course to be too basic and lacking in advanced topics
Limited interaction and feedback from instructors

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