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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

AI in Healthcare Specialization

Instructors: Matthew Lungren

5 course series

Get in-depth knowledge of a subject

4.7

(2,305 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

5 course series

Get in-depth knowledge of a subject

4.7

(2,305 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Identify problems healthcare providers face that machine learning can solve
Analyze how AI affects patient care safety, quality, and research
Relate AI to the science, practice, and business of medicine
Apply the building blocks of AI to help you innovate and understand emerging technologies

Overview

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.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

90 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Stanford University

Specialization - 5 course series

The final course will consist of a capstone project that will take you on a guided tour exploring all the concepts we have covered in the different classes. This will be a hands-on experience following a patient's journey from the lens of the data, using a unique dataset created for this specialization.We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model.

Introduction to Healthcare

Course 111 hours4.8 (1,389 ratings)View course

What you'll learn

The major challenges of the U.S.healthcare system
Issues you may encounter in efforts to improve healthcare delivery and the healthcare system
Who the key stakeholders are in the U.S. healthcare system

Skills you'll gain

Health Care, Medicaid, Medicare, Health Policy, Healthcare Industry Knowledge, Medical Billing, Pharmaceuticals, Value-Based Care, Hospital Experience, Health Care Procedure and Regulation, Healthcare Ethics, Health Care Administration, Health Systems, and Managed Care

Introduction to Clinical Data

Course 211 hours4.6 (469 ratings)View course

What you'll learn

How to apply a framework for medical data mining
Ethical use of data in healthcare decisions
How to make use of data that may be inaccurate in systematic ways
What makes a good research question and how to construct a data mining workflow answer it

Skills you'll gain

Electronic Medical Record, Feature Engineering, Clinical Data Management, Data Mining, Health Informatics, Text Mining, Data Transformation, Medical Imaging, Data Ethics, Data Collection, Clinical Research, Data Processing, Unstructured Data, and Health Care

Fundamentals of Machine Learning for Healthcare

Course 314 hours4.8 (593 ratings)View course

What you'll learn

Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.
Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
Learn important approaches for leveraging data to train, validate, and test machine learning models.
Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.

Skills you'll gain

Machine Learning Algorithms, Machine Learning, Applied Machine Learning, Healthcare Industry Knowledge, Healthcare Ethics, Health Policy, Artificial Neural Networks, Medical Science and Research, Supervised Learning, Health Informatics, Health Care, Data Ethics, Deep Learning, Reinforcement Learning, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), and Responsible AI

Evaluations of AI Applications in Healthcare

Course 411 hours4.5 (318 ratings)View course

What you'll 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

Skills you'll gain

Responsible AI, Health Technology, Continuous Monitoring, Clinical Informatics, Healthcare Industry Knowledge, Regulatory Compliance, Health Equity, Health Informatics, Decision Support Systems, Application Deployment, Clinical Assessment, Predictive Modeling, Data Ethics, Clinical Research Ethics, and AI Personalization

AI in Healthcare Capstone

Course 510 hours4.7 (289 ratings)View course

What you'll learn

This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Skills you'll gain

Machine Learning, Applied Machine Learning, Artificial Intelligence, Performance Tuning, Responsible AI, Risk Modeling, Data Validation, Feature Engineering, Healthcare Industry Knowledge, Clinical Data Management, Data Ethics, Analysis, Patient-centered Care, Health Informatics, Health Care Procedure and Regulation, Data Collection, Healthcare Ethics, and Application Deployment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Matthew Lungren

Stanford University

2 Courses41,889 learners

Serena Yeung

Stanford University

2 Courses41,889 learners

Mildred Cho

Stanford University

2 Courses90,613 learners

Tina Hernandez-Boussard

Stanford University

2 Courses29,953 learners

Offered by

Stanford University

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Frequently asked questions

This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

AI in Healthcare Specialization

What you'll learn

Overview

Skills you'll gain

What’s included

Advance your subject-matter expertise