Related AI/ML in Medical Education Pages
Artificial intelligence (AI) is reshaping health care at a breakneck pace. From reducing administrative burdens to enabling next-generation clinical decision support, AI is transforming how care is delivered, coordinated, and experienced. STFM and other family medicine organizations have laid essential groundwork to build AI capacity across the discipline. In 2021, the American Board of Family Medicine (ABFM) and the Center for Professionalism and Value in Health Care convened an AI summit that brought together early thought leaders to explore opportunities and challenges related to AI in primary care. This catalyzed in 2022 with the ABFM Foundation’s “Enterprise AI and Building Long-Term (EnAIBL) Capacity for FM” Initiative, a national collaborative supporting family medicine departments in strengthening the people, infrastructure, and processes needed to harness AI’s potential.In 2023, through an effort funded by the Gordon and Betty Moore Foundation and facilitated by ABFM, an AI Bootcamp series launched at the NAPCRG Annual Meeting. This was followed in 2024 by the development and dissemination of STFM's AI and Machine Learning for Primary Care (AiM-PC) Curriculum. Additional momentum came from the American Academy of Family Physicians (AAFP) and Rock Health AI Starfield Summit in May 2025.An STFM AI in Medical Education Task Force, chaired by STFM Past President Steven Lin, MD, is working on the following tactics to advance responsible, outcome-driven, and people-centered artificial intelligence:
- Forge new partnerships with other professional societies, health systems, industry, payers, and government around AI
- Identify and promote foundational AI use cases that help the family medicine workforce
- Conduct a comprehensive internal analysis of opportunities for incorporating or strengthening AI content into existing STFM programs
- Identify and promote opportunities for AI to lower the burden of education administration and curriculum development for faculty
- Conduct a national landscape analysis to identify case studies/best practices for how to elevate family medicine educators to AI leadership roles, and how to build and spread primary care
- Publish a “Family Medicine AI Playbook” with practical guidance for organizations
- Develop a framework for Family Medicine AI Centers of Excellence
- Identify and promote opportunities to elevate members to leadership roles in AI
Artificial Intelligence (AI) Playbook: “Building AI Excellence in Primary Care”
This free AI Playbook, developed by the STFM AI in Medical Education Task Force, describes the emerging building blocks of AI excellence in family medicine and offers practical guidance for organizations at different stages of maturity. The goal is to help organizations see where they are, where they might go next, and how AI can be shaped in ways that support care quality, equity, workforce development, learning health systems, and community trust.The Playbook is an implementation-oriented field resource for family medicine organizations. It can be used by department and organizational leaders, clinicians, educators, residency and clerkship leaders, researchers, informatics and data partners, operational leaders, quality and implementation teams, community partners, and others involved in shaping responsible AI use.Use the Playbook to:
- Orient leaders and teams to the major domains of AI excellence in family medicine
- Build shared language for discussing responsible AI capability
- Guide internal strategic planning and priority-setting
- Identify local strengths, gaps, and next steps
- Support resource requests for governance, training, infrastructure, implementation, and evaluation
- Inform conversations with institutional partners, including IT, informatics, compliance, legal, education, research, and clinical operations
- Benchmark emerging practices across the field
- Spark local discussion about how AI can support care quality, equity, workforce sustainability, learning, and trust
Development of the Playbook was supported by a grant from the American Board of Family Medicine Foundation.
STFM Artificial Intelligence in Medical Education Task Force
- Steven Lin, MD, Stanford University — Chair
- Rika Bajra, MD, Stanford University
- Ian Bennett MD, PhD, University of Washington
- Linda Chang, PharmD, MPH, MHPE, BCPS, University of Illinois at Rockford
- Enitza George, MD, MBA, MSAI, SUNY Downstate Health Sciences University
- Karim Hanna, MD, University of Southern Florida TGH FMR Program
- John Hayes, DO, MCW-Prevea Green Bay FMR Program
- Misbah Keen, MD, MBI, MPH, University of Washington
- Winston Liaw, MD, MPH, University of Houston
- May Lin, DO, Touro University
- Yun Shi, MD, PhD, University of Texas Health, San Antonio
- Margaret Ann Smith, MBA, Stanford University
- Brent Sugimoto, MD, MPH, LifeLong Medical Care FMR Program
- Rod Suman, Society of Teachers of Family Medicine
- Mary Theobald, MBA, Society of Teachers of Family Medicine
- Timothy Tsai, DO, MMCi, Stanford University
- Steven Waldren, MD, American Academy of Family Physicians
- Yun Liu, PhD, Google Research
