May 12, 2025—STFM released the fourth and final part of the Artificial Intelligence and Machine Learning for Primary Care Curriculum (AiM-PC).
This latest release is titled “Clinical Implementation of AI/ML” and includes three online modules. They are titled:
- “Evidence-Based Evaluation of AI-Based Tools”, by Jacqueline K. Kueper, PhD
- “AI-Enhanced Clinical Encounters”, by Cornelius A. James, MD
- “Integrating AI/ML Into the Clinic”, by Winston Liaw, MD, MPH
All three are approximately 1 hour in length and approved for 1 CME credit from the American Academy of Family Physicians.
How to Access the AI/ML Curriculum Modules
The AI/ML Curriculum is free for people with an STFM membership and online account. Log in to your account to enroll. Once you have enrolled in the course, go to your stfm.org profile page and click Online Learning at the top.
In order to get the full experience of these courses, we recommend that the courses be viewed on a desktop computer, laptop, or tablet. Smartphones are not supported at this time.
Free AI/ML Webinar on May 30
STFM is hosting a webinar on Friday, May 30 centered around the ethical use of AI in family medicine practice. The 1-hour webinar begins at 12 pm CDT/1 pm EDT.
During the webinar, panelists will discuss the opportunities and challenges of ethically integrating AI/ML into patient care. Critical topics around using AI/ML in the clinic, such as bias, privacy, harms, and trustworthiness, will be discussed. Panelists will provide practical strategies for responsible implementation of AI/ML in the clinic.
More About the AI/ML Curriculum
The AI/ML Curriculum aims to equip learners with the skills needed to be engaged stakeholders, use AI/ML in their practice, and ensure responsible and ethical use of AI/ML. Developed for medical students, primary care residents, faculty, and primary care physicians, the online curriculum includes four parts:
- Interview With an Innovator, Series 1
- Foundations of AI/ML in the Primary Care Clinic
- Interview With an Innovator, Series 2
- Clinical Implementation of AI/ML
Development of these modules was funded by the Gordon and Betty Moore Foundation and the American Board of Family Medicine (ABFM) Foundation.