Stanford University - Evaluations of AI Applications in Healthcare

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Evaluations of AI Applications in Healthcare
at
Coursera
Overview

Duration	11 hours
Total fee	Free
Mode of learning	Online
Difficulty level	Beginner
Official Website	Explore Free Course
Credential	Certificate

Evaluations of AI Applications in Healthcare
at
Coursera
Highlights

This Course Plus the Full Specialization.
Shareable Certificates.
Graded Programming Assignments.

Evaluations of AI Applications in Healthcare
at
Coursera
Course details

More about 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.
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.

Evaluations of AI Applications in Healthcare
at
Coursera
Curriculum

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

Study Guide Module 1

Citations and Additional Readings

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

Citations and Additional Readings

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

Citations and Additional Readings

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

Citations and Additional Readings

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

Citations and Additional Readings

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

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Evaluations of AI Applications in Healthcare
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Students Ratings & Reviews

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Adinarayana N

Evaluations of AI Applications in Healthcare

Offered by Coursera

Learning Experience: 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.

Faculty: Great Faculty well explained Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you'll get a sense of how AI could transform patient care and diagnoses.

Course Support: I'm still look for options

Reviewed on 8 Oct 2022Read More