Fundamentals of Machine Learning for Healthcare
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Fundamentals of Machine Learning for Healthcare
This course is part of AI in Healthcare Specialization
Instructors: Matthew Lungren
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633 reviews
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
- Artificial Neural Networks
- Health Informatics
- Healthcare Industry Knowledge
- Applied Machine Learning
- Deep Learning
- Generative Model Architectures
- Machine Learning Methods
- Statistical Machine Learning
- Medical Science and Research
- Healthcare Ethics
- Artificial Intelligence and Machine Learning (AI/ML)
- Model Evaluation
- Responsible AI
- Machine Learning Algorithms
- Machine Learning
- Reinforcement Learning
- Health Policy
- Data Ethics
- Model Training
- Supervised Learning
Details to know
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- Earn a shareable career certificate
There are 8 modules in this course
Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.
This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Co-author: Geoffrey Angus Contributing Editors: Mars Huang Jin Long Shannon Crawford Oge Marques 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.
What's included
7 videos6 readings3 assignments
7 videos•Total 34 minutes
- Why machine learning in healthcare?•3 minutes
- History of AI in Medicine•6 minutes
- Course Overview•4 minutes
- Why Healthcare Needs Machine Learning•2 minutes
- Machine Learning Magic•9 minutes
- Machine Learning, Biostatistics, Programming•4 minutes
- Can Machine Learning Solve Everything?•6 minutes
6 readings•Total 20 minutes
- Getting Started: Creators of This Course•5 minutes
- Video Image Credit•0 minutes
- Video Image Credit•0 minutes
- Study Guide Module 1•5 minutes
- Citations and Additional Readings•10 minutes
- Video Image Credit•0 minutes
3 assignments•Total 55 minutes
- Reflection Exercise•15 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
8 videos2 readings3 assignments
8 videos•Total 51 minutes
- Machine Learning Terms, Definitions, and Jargon Part 1•11 minutes
- Machine Learning Terms, Definitions, and Jargon Part 2•10 minutes
- How Machines Learn Part 1•9 minutes
- How Machines Learn Part 2•6 minutes
- Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem•3 minutes
- Other Traditional Supervised Machine Learning Approaches•3 minutes
- Support Vector Machine (SVM)•4 minutes
- Unsupervised Machine Learning•4 minutes
2 readings•Total 10 minutes
- Study Guide Module 2•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 50 minutes
- Reflection Exercise•10 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
10 videos3 readings3 assignments
10 videos•Total 63 minutes
- Introduction to Deep Learning and Neural Networks•8 minutes
- Deep Learning and Neural Networks•7 minutes
- Cross Entropy Loss•3 minutes
- Gradient Descent•6 minutes
- Representing Unstructured Image and Text Data•6 minutes
- Convolutional Neural Networks•4 minutes
- Natural Language Processing and Recurrent Neural Networks•7 minutes
- The Transformer Architecture for Sequences•4 minutes
- Commonly Used and Advanced Neural Network Architectures•6 minutes
- Advanced Computer Vision Tasks and Wrap-Up•10 minutes
3 readings•Total 10 minutes
- Video Image Credit•0 minutes
- Study Guide Module 3•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 50 minutes
- Reflection Exercise•10 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
5 videos2 readings3 assignments
5 videos•Total 35 minutes
- Introduction to Model Performance Evaluation•3 minutes
- Overfitting and Underfitting•9 minutes
- Strategies to Address Overfitting, Underfitting and Introduction to Regularization •5 minutes
- Statistical Approaches to Model Evaluation•6 minutes
- Receiver Operator and Precision Recall Curves as Evaluation Metrics•12 minutes
2 readings•Total 10 minutes
- Study Guide Module 4•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 50 minutes
- Reflection Exercise 1•10 minutes
- Reflection Exercise 2•10 minutes
- Knowledge Check•30 minutes
What's included
9 videos2 readings3 assignments
9 videos•Total 50 minutes
- Introduction to Common Clinical Machine Learning Challenges•6 minutes
- Utility of Causative Model Predictions•3 minutes
- Context in Clinical Machine Learning•8 minutes
- Intrinsic Interpretability •4 minutes
- Medical Data Challenges in Machine Learning Part 1•6 minutes
- Medical Data Challenges in Machine Learning Part 2•6 minutes
- How Much Data Do We Need?•3 minutes
- Retrospective Data in Medicine and "Shelf Life" for Data•6 minutes
- Medical Data: Quality vs Quantity•9 minutes
2 readings•Total 10 minutes
- Study Guide Module 5•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 50 minutes
- Reflection Exercise•10 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
6 videos4 readings3 assignments
6 videos•Total 41 minutes
- Clinical Utility and Output Action Pairing•6 minutes
- Taking Action - Utilizing the OAP Framework•6 minutes
- Building Multidiciplinary Teams for Clinical Machine Learning •9 minutes
- Governance, Ethics, and Best Practices•6 minutes
- On Being Human in the Era of Clinical Machine Learning•8 minutes
- Death by GPS and Other Lessons of Automation Bias•5 minutes
4 readings•Total 25 minutes
- Study Guide Module 6•5 minutes
- Citations and Additional Readings•5 minutes
- Video Image Credit•0 minutes
- Recommended Reading for Ethics•15 minutes
3 assignments•Total 50 minutes
- Reflection Exercise•10 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
8 videos
8 videos•Total 97 minutes
- Introduction to Foundation Models•13 minutes
- Adapting to Technology•7 minutes
- General AI and Emergent Behavior•10 minutes
- How Foundation Models Work•22 minutes
- Healthcare Use Cases for Text Data•16 minutes
- Healthcare Use Cases for Non-textual Unstructured Data•12 minutes
- Challenges and Pitfalls•14 minutes
- Conclusion•4 minutes
What's included
1 video3 readings1 assignment
1 video•Total 7 minutes
- Wrap Up and Goodbyes•7 minutes
3 readings•Total 15 minutes
- Final Assessment Note•5 minutes
- Claim CME Credit•0 minutes
- Full Study Guide•10 minutes
1 assignment•Total 60 minutes
- Final Assessment•60 minutes
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Reviewed on Sep 20, 2025
As you said in the goodbye video, you made this with love.
This course is worth more than 5 stars. Thank you, I can't be more grateful!
Reviewed on May 7, 2023
Excellent introduction to ML and AI in the context of healthcare. Loaded with information without being overwhelming
Reviewed on Nov 11, 2020
Completing this course has given me a solid foundation and confidence to engage at a deeper level with AIML in health, both as a student and exponent thereof.
Frequently asked questions
Dates and Duration
Original Release Date: 08/10/2023
Expiration Date: 08/10/2026
Estimated Time to Complete: 11 hours
CME Credits Offered: 11.00
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 11.00 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.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. 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.
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.
More questions
Financial aid available,