University of Michigan
Data Augmented Technology Assisted Medical Decision Making
University of Michigan

Data Augmented Technology Assisted Medical Decision Making

Taught in English

Course

Gain insight into a topic and learn the fundamentals

Cornelius James

Instructor: Cornelius James

Beginner level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe the crucial role, strengths, limitations of AI and ML in evidence-based medical decision making

  • Evaluate machine learning studies for bias and systematic error to enhance diagnostic decisions.

  • Apply the results of machine learning studies and outputs to diagnostic decisions.

  • Identify legal and ethical issues and best practices for AI and ML use in healthcare settings

Details to know

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Recently updated!

April 2024

Assessments

18 assignments

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There are 4 modules in this course

In week 1, you will be introduced to artificial intelligence (AI) and machine learning (ML) and the vocabulary necessary to effectively communicate with relevant stakeholders. You will learn about some of the applications of AI/ML in healthcare and the challenges associated with using these technologies in healthcare.

What's included

17 videos5 readings5 assignments1 discussion prompt

In week 2 you will learn the concepts and statistical measures necessary for interpretation of results of diagnostic studies that include ML.

What's included

15 videos3 readings5 assignments1 discussion prompt

In week 3, you will develop the skills necessary to critically evaluate diagnostic studies that include AI/ML. This week emphasizes the skills necessary to efficiently and effectively use AI/ML to augment diagnostic decisions. step.

What's included

14 videos3 readings2 assignments1 discussion prompt

In the final week of this course, you will review the current legal and ethical landscape of AI/ML in medicine, possible social biases that may be perpetuated by AI/ML algorithms, and recommendations for avoiding these.

What's included

15 videos3 readings6 assignments1 discussion prompt

Instructor

Cornelius James
University of Michigan
1 Course110 learners

Offered by

Recommended if you're interested in Machine Learning

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