Model Development, Deployment, and Ethical Considerations

Monday, August 12, 2024, 12:00 AM - Wednesday, August 11, 2027, 11:59 PM, On Demand

This comprehensive course covers various machine-learning techniques in the medical field. It starts with the basics of clinical data analysis and explores predictive modeling, model deployment, deep learning, and the ethical and practical considerations of their applications. You will delve into various topics, from predictive analysis and the fundamentals of machine learning to advanced concepts in deep learning with neural networks and applying these models to electronic health records. The course also navigates the complexities of unsupervised learning and feature engineering and concludes with a discussion on the utility, ethics, and liabilities of deploying machine learning solutions in healthcare.

  Release Date: August 12, 2024
  Expiration Date: August 11, 2027
  Estimated Time to Complete: 10.0 hours
  Registration Fee:  $675 per course; $1299 All-Access Plan

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

View entire series: This is the second course in our Applications of Machine Learning in Medicine program. View the first course in the program: Data Foundations for Machine Learning in Medicine

AMA PRA Category 1 Credits™ (10.00 hours), Non-Physician Participation Credit (10.00 hours)

1. Discuss the foundational elements of clinical data analysis and the initial steps in developing predictive models using medical data.
2. Explore advanced machine learning and deep learning techniques, including neural networks, and their applications in healthcare.
3. Describe the process of deploying machine learning models within healthcare settings, including integration with existing healthcare systems and overcoming operational challenges.
4. Recognize the ethical implications of machine learning in healthcare, focusing on privacy, bias, and the implications of automated decision-making.
5. Develop unsupervised learning techniques and feature engineering to enhance the predictive power and reliability of machine learning models in healthcare contexts.

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 10.00 AMA PRA Category 1 Credits^TM.  Physicians should claim only the credit commensurate with the extent of their participation in the activity. 

Accessibility Statement
 Stanford University School of Medicine is committed to ensuring that its programs, services, goods and facilities are accessible to individuals with disabilities as specified under Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Amendments Act of 2008.  If you have needs that require special accommodations, please contact the CME.

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: https://laneguides.stanford.edu/multicultural-health

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

Stanford Medicine adheres to the Standards for Integrity and Independence in Accredited Continuing Education.

The content of this activity is not related to products or the business lines of an ACCME-defined ineligible company. Hence, there are no relevant financial relationships with an ACCME-defined ineligible company for anyone who was in control of the content of this activity.