Online Course. 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.
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.
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.
What You Will 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.
Week 1: Why machine learning in healthcare?
Week 2: Concepts and Principles of machine learning in healthcare part 1
Week 3: Concepts and Principles of machine learning in healthcare part 2
Week 4: Evaluation and Metrics for machine learning in healthcare
Week 5: Strategies and Challenges in Machine Learning in Healthcare
Week 6: Best practices, teams, and launching your machine learning journey
Week 7: Course Conclusion
Co-author: Geoffrey Angus
- Mars Huang
- Jin Long
- Shannon Crawford
- Oge Marques
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.
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.
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Financial aid available
- This Course Plus the Full Specialization
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- Practice Quizzes
- Graded Assignments with Peer Feedback
- Graded Quizzes with Feedback
- Graded Programming Assignments
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