The Artificial Intelligence and Machine Learning for Primary Care (AiM-PC) Curriculum for medical students, primary care residents, and practicing primary care clinicians was developed in conjunction with the Society of Teachers of Family Medicine (STFM) and American Board of Family Medicine, funded by the Gordon and Betty Moore Foundation, and supported by an advisory committee. Our team consists of educators with expertise in primary care, computer science, instructional design, and ethics. By harnessing these diverse perspectives, AiM-PC will 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.
The modules in this course include:
- Module 1: AI/ML Essentials
- Module 2: Social and Ethical Implications of AI/ML
- Module 3: Evidence-Based Evaluation of AI/ML-Based Tools
- Module 4: AI/ML-Enhanced Clinical Encounters
- Module 5: Integrating AI/ML into the Clinic
While these modules are numbered, you can take them in any order you like. We recommend you begin with the first module.
Our goals for this curriculum are:
- To equip learners with the skills necessary to be engaged stakeholders in a clinical world that includes AI/ML
- To develop and disseminate innovative health care AI/ML curricula to prepare the primary care workforce
- To create efficient and effective AI/ML curricula that can be applied across various primary care settings
- To prepare learners in primary care settings to appraise and apply literature and outputs in their clinical practice
- To ensure that all patients benefit from AI/ML-augmented clinical care
In this series of interviews, three experts draw on their diverse experience with AI both in the clinic and behind the scenes.
Total running time, 2.5 hours:
Each video includes chapters which are viewable from a button at the bottom right of the player.
Interview with an Innovator, Episode 1: Steven Lin, MD
Steven Lin, MD, is the chief of general primary care and the head of technology innovation for the Division of Primary Care and Population Health at Stanford University School of Medicine. His focus is on the intersection of health services innovation, digital health and emerging technologies — specifically artificial intelligence and machine learning in healthcare. Steven is the founding director of the Stanford Healthcare AI Applied Research Team, a primary care-focused AI/ML implementation center partnered with 50 organizations across industry, academia, non-profit and government to bring leading-edge AI/ML technologies from “code to beside” in support of the Quintuple Aim. He was a James C. Puffer/American Board of Family Medicine fellow with the National Academy of Medicine and is the author of over 450 scholarly works and conference presentations.
Interview with an Innovator, Episode 2: Bob Wachter, MD
Robert M. Wachter, MD, is professor and chair of the Department of Medicine at the University of California, San Francisco (UCSF). Author of 300 articles and six books, Wachter coined the term “hospitalist” in 1996 and is often considered the “father” of the hospitalist field, the fastest growing specialty in the history of modern medicine. He is past president of the Society of Hospital Medicine, past chair of the American Board of Internal Medicine, and an elected member of the National Academy of Medicine. In 2015, Modern Healthcare magazine ranked him as the most influential physician-executive in the United States. His 2015 book, The Digital Doctor: Hope, Hype and Harm at the Dawn of Medicine’s Computer Age, was a New York Times science bestseller. In 2020-2023, Wachter’s tweets on Covid-19 were viewed more than 500 million times by 275,000 followers and served as a trusted source of information on the clinical, public health, and policy issues surrounding the pandemic. His next book, A Giant Leap: How AI Will Transform Healthcare – and What it Means for the Future, will be published in about 18 months by Portfolio, a Penguin imprint.
Interview with an Innovator, Episode 3: Katie Link
Katie Link is a product manager at NVIDIA , where she helps enable healthcare companies and researchers to solve real-world healthcare challenges with large language models (LLMs) and other advanced technologies. Prior to NVIDIA, she led healthcare and life sciences applications of artificial intelligence as a Machine Learning Engineer at Hugging Face, an open source AI startup. She is currently based in Seattle, Washington and is on leave as a medical student at the Icahn School of Medicine at Mount Sinai. While in medical school, she led artificial intelligence research at NYU Langone Hospital, creating the largest open dataset of magnetic resonance imaging (MRI) for brain metastases and developing novel deep learning algorithms for tracking cancer progression. In her spare time, she also works on AI education initiatives for medical trainees. Prior to medical school, she was an AI Resident at Google X. She holds a bachelor’s degree in Neuroscience with a minor in Computer Science from Johns Hopkins University.
