AI Series: Fundamentals of AI and Machine Learning in Healthcare

Internet Enduring Material Sponsored by Stanford University School of Medicine. Presented by the Center for Health Education at Stanford University School of Medicine.  Advancements of machine learning and AI into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. But we will never realize the potential for these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles - this will allow successful, responsible development and deployment of these systems into the healthcare domain.

The focus of this course is on the key concepts and principles rather than programming or engineering implementation. Those with backgrounds in healthcare, health policy, healthcare system leadership, pharmaceutical, and clinicians as well as those with data science backgrounds who are new to healthcare applications will be empowered with the knowledge to responsibly and ethically evaluate, critically review, and even use these technologies in healthcare. We will cover machine learning approaches, medical use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing, building, and evaluating machine learning in healthcare applications.

  Original Release Date: August 10, 2020
  Review Date: August 8, 2023
  Expiration Date: August 9, 2026
  Estimated Time to Complete: 11.0 Hours

Click Begin (at the top) to learn more about how to enroll in the course.

View entire series: https://www.coursera.org/specializations/ai-healthcare

1. Evaluate the relationships between the fields of machine learning, biostatistics, and traditional computer programming.
2. Review advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.
3. Develop approaches for leveraging data to train, validate, and test machine learning models.
4. Consider how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.

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.

Credit Designation

American Medical Association (AMA)
Stanford Medicine designates this Enduring Material for a maximum of 11.00 AMA PRA Category 1 Credits^TM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Cultural and Linguistic Competency
The planners and speakers of this CME activity have been encouraged to address cultural issues relevant to their topic area for the purpose of complying with California Assembly Bill 1195. Moreover, the Stanford University School of Medicine Multicultural Health Portal contains many useful cultural and linguistic competency tools including culture guides, language access information and pertinent state and federal laws.  You are encouraged to visit the Multicultural Health Portal: http://lane.stanford.edu/portals/cultural.html

Reference/Bibliography List
For additional resources, please visit the course.

For activity related questions, please contact
  Ph: 650.204.3984
  Email: [email protected]

For CME general questions, please contact 
   Email: [email protected]

Mitigation of Relevant Financial Relationships