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AI Series: Fundamentals of AI and Machine Learning in Healthcare


AI Series: Fundamentals of AI and Machine Learning in Healthcare Banner

  • Overview
  • Faculty
  • Begin
Add to Calendar AI Series: Fundamentals of AI and Machine Learning in Healthcare 8/10/2023 12:00:00 AM 8/9/2026 11:59:00 PM America/Los_Angeles For More Details: https://stanford.cloud-cme.com/course/courseoverview?P=0&EID=47055 Description: 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 potenti... On Demand false MM/DD/YYYY


Date & Location
Thursday, August 10, 2023, 12:00 AM - Sunday, August 9, 2026, 11:59 PM, On Demand

Overview

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.


Registration

  Release Date: August 10, 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



Credits
AMA PRA Category 1 Credits™ (11.00 hours), Non-Physician Participation Credit (11.00 hours)

Target Audience
Specialties - Non-clinical
Professions - Advance Practice Nurse (APN), Allied Dental Professional, Athletic Trainer, Counselor, Dentist, Dietetic Technician Registered (DTR), Fellow/Resident, Industry, Non-Physician, Nurse, Optometrist , Pharmacist, Pharmacy Technician , Physical Therapist, Physician, Physician Associate, Psychologist, Registered Dietitian, Registered Nurse (RN), Social Worker, Student


Objectives
At the conclusion of this activity, participants should be able to:

  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.

Accreditation

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 CreditsTM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.


Additional Information

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


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. 

Faculty Member Information
Role in activity
Name of Ineligible Company(s) / Nature of Relationship(s)
Matt Lungren, MD
Associate Professor of Radiology
Stanford University School of Medicine
Course Director, Faculty

AI Series: Fundamentals of AI and Machine Learning in Healthcare
INSTRUCTIONS: Click "Launch Website" to enroll on our external learning management system (LMS). With successful completion at the end of the course, an evaluation and claim credit url link will be provided to you to access the Stanford CME MY CE Portal with more detailed instructions.
Launch Video

 

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