This course is part of the AI in Healthcare Specialization

4.5
stars
141 ratings

Tina Hernandez-Boussard +1 more instructor

7,629 already enrolled

Offered By

AI in Healthcare SpecializationStanford University

About this Course

16,404 recent views

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 5 in the

AI in Healthcare Specialization

Beginner Level

Approx. 10 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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 5 in the

AI in Healthcare Specialization

Beginner Level

Approx. 10 hours to complete

English

Instructors

Instructor rating

4.47/5 (52 Ratings)

Tina Hernandez-Boussard

Associate Professor

Medicine (Biomedical Informatics), of Biomedical Data Science and of Surgery

9,191 Learners

2 Courses

Mildred Cho

Professor

Department of Pediatrics Division of Medical Genetics

30,105 Learners

2 Courses

Offered by

Stanford University

Syllabus - What you will learn from this course

Week1

Week 1

1 hour to complete

AI in Healthcare

1 hour to complete

10 videos (Total 30 min), 2 readings, 3 quizzes

Week2

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

Learning Objectives1mRecap: Framework1mStakeholders1mClinical Utility1mOutcome: Action Pairing, An Overview4mLead Time1mType of Action3mOAP Examples3mNumber Needed to Treat2mNet Benefits2mDecision Curves4mFeasibility overview4mImplementation Costs1mClinical Evaluation and Uptake4mSummary1m

2 readings

Study Guide Module 25mCitations and Additional Readings

4 practice exercises

Reflection Exercise 110mReflection Exercise 210mReflection Exercise 310mKnowledge Check30m

Week3

Week 3

2 hours to complete

AI Deployment

2 hours to complete

19 videos (Total 47 min), 2 readings, 5 quizzes

19 videos

Learning Objectives1mThe Problem1mPractical Questions Prior to Deployment3mDeployment Pathway1mDesign and Development3mStakeholder Involvement2mData Type and Sources3mSettings3mIn Silico Evaluation2mNet Utility & Work Capacity2mStatistical Validity1mCare Integration, Silent Mode2mClinical Integration, Considerations2mTechnical Integration1mDeployment Modalities2mContinuous Monitoring and Maintenance2mChallenges of Deployment5mSepsis Example1mSummary1m

2 readings

Study Guide Module 35mCitations and Additional Readings

5 practice exercises

Reflection Exercise 110mReflection Exercise 210mReflection Exercise 310mReflection Exercise 410mKnowledge Check30m

Week4

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

Learning Objectives2mReal World Examples of AI Bias4mIntroduction - Types of Bias41sHistorical Bias2mRepresentation Bias1mMeasurement Bias1mAggregation Bias2mEvaluation Bias1mDeployment Bias1mWhat is algorithmic Fairness2mAnti-classification2mParity Classification1mCalibration2mApplying Fairness Measures2mLack of Transparency2mMinimal Reporting Standards3mOpportunities and Challenges2mSummary2m

2 readings

Study Guide Module 45mCitations and Additional Readings

5 practice exercises

Reflection Exercise 110mReflection Exercise 210mReflection Exercise 310mReflection Exercise 410mKnowledge Check30m

Reviews

4.5

36 reviews

5 stars
70.21%
4 stars
18.43%
3 stars
6.38%
2 stars
2.83%
1 star
2.12%

TOP REVIEWS FROM EVALUATIONS OF AI APPLICATIONS IN HEALTHCARE

by PJJul 9, 2022

Useful content, but there is a lot of repetition early in the course.

by DDec 7, 2020

I was expecting the Medical genetics professor as a teacher also.

by JWApr 7, 2022

This course was really valuable for linking and embedding my knowledge gained by reading FDA guidance documents and knowledge sharing from my Quality Assurance and Regulatory Affairs colleagues

by KNDec 2, 2020

More examples would have been better to understand some of the concepts.

View all reviews

About the AI in Healthcare Specialization

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.

In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines. CME 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. View the full CME accreditation information on the individual course FAQ page.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
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.
Is this activity accredited for Continuing Medical Education (CME)?
Dates and Duration
Original Release Date: 08/10/2020
Expiration Date: 08/10/2023
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.

More questions? Visit the Learner Help Center.

Evaluations of AI Applications in Healthcare

About this Course

What you will learn