KEY INITIATIVES

Artificial Intelligence in Medical Education Initiative

Artificial intelligence and machine learning (AI/ML) are transforming primary care, and learners want to participate in the revolution. An STFM Artificial Intelligence in Medical Education Task Force, chaired by STFM President Steven Lin, MD, is working on tactics to advance responsible, outcome-driven, and people-centered artificial intelligence in medical education and primary care.

AI in Primary Care Curriculum AI Centers of Excellence Recognition Program

Artificial intellence (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. Family medicine has been part of this transformation from the start, shaping how AI advances equity, access, and whole-person care.Over the past 4 years, the 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.Aligned with this call, STFM’s AI in Medical Education Task Force—in collaboration with the Association of Departments of Family Medicine (ADFM) and with funding from the ABFM Foundation—is leading a multi-year initiative to establish a national framework for Family Medicine AI Centers of Excellence (CoE). The goal is to help organizations build, sustain, and integrate AI capacity across clinical care, education, and research, not as separate domains but as integrated capabilities that reflect the breadth and impact of our discipline.An STFM Artificial Intelligence in Medical Education Task Force, chaired by STFM President Steven Lin, MD, is working on the following tactics to advance responsible, outcome-driven, and people-centered artificial intelligence:

  • Identify and act on opportunities for collaborative work around AI within STFM (e.g. the AI in Education Collaborative and other Collaboratives)
  • Identify and act on opportunities to collaborate around AI with the larger family of family medicine organizations
  • 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
  • Identify opportunities to develop datasets that catalyze family medicine R&D and attract industry
  • Identify and promote opportunities to apply AI to support core values such as equity and community
  • Support the work of family medicine AI pioneers, disseminate learnings, and promote the development of new centers of excellence
  • Provide members with opportunities to practice with exemplar AI-based tools that can be applied to a wide range of clinical, educational, and research settings
  • Address key limitations to the use of AI including unintended, harmful consequences
  • Identify and promote opportunities for AI to personalize the learning journey for learners (“precision medical education”)
  • Identify and promote opportunities for AI to lower the burden of education administration and curriculum development for faculty
  • Identify and promote opportunities to elevate members to leadership roles in AI
  • Identify or create new opportunities to incorporate AI content/training into existing STFM programs and offerings for medical students, residents, and 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
  • Inspire and mobilize STFM members and more broadly, frontline primary care clinicians, scholars, educators, and learners around AI

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

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Events

July 22, 2026, 1 pm eastern: Equitable Advisors Webinar: "How to Keep More of What You Make: Managing Your Tax Bracket"

August 26, 2026, 1 pm eastern: Webinar: "Using Mobile Apps for Competency-Based Direct Observation and Outcome Assessment"

August 31–September 2, 2026: STFM Conference on Practice & Quality Improvement

September 14, 2026, 3 pm eastern: Webinar: "Rural Health Government Update with Nina DeJonghe, MPP"

October 2, 2026, 1 pm eastern: Webinar: "Tracks and Areas of Concentration in Family Medicine Residencies: History, Evolution, and Impact"

February 5–7, 2027: STFM Conference on Medical Student Education

May 1–5, 2027: STFM Annual Spring Conference

Deadlines

July 21, 2026: Submit Questions for CERA Survey of Program Directors

July 24, 2026: Apply for Openings on STFM Board of Directors

July 31, 2026: Apply to Serve as Resident or Student Representative on STFM Board of Directors

August 14, 2026: Nominate for the F. Marian Bishop Leadership Award

August 18, 2026: Apply to Join STFM Medical Student Educators Development Institute Fellowship

August 21, 2026: Apply to Be Faculty for STFM Emerging Leaders Fellowship

August 21, 2026: Apply to Be a PRiMER Journal Associate Editor

August 31, 2026: Submit 60-Word Stories to Be Published in Family Medicine for STFM's 60th Anniversary

August 31, 2026: Nominate Yourself or Another Member for STFM Bishop Society

September 4, 2026: Submit to Present at 2027 STFM Annual Spring Conference

October 1, 2026: Nominate Mentors for STFM 60th Anniversary Mentor Recognition Award

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AI Chatbot Tips

Tips for Using STFM's AI Assistant

STFM's AI Assistant is designed to help you find information and answers about Family Medicine education. While it's a powerful tool, getting the best results depends on how you phrase your questions. Here's how to make the most of your interactions:

1. Avoid Ambiguous Language

Be Clear and Specific: Use precise terms and avoid vague words like "it" or "that" without clear references.

Example:

Instead of: "Can you help me with that?"
Try: "Can you help me update our Family Medicine clerkship curriculum?"
Why this is important: Ambiguous language can confuse the AI, leading to irrelevant or unclear responses. Clear references help the chatbot understand exactly what you're asking.

2. Use Specific Terms

Identify the Subject Clearly: Clearly state the subject or area you need information about.

Example:

Instead of: "What resources does STFM provide?"
Try: "I'm a new program coordinator for a Family Medicine clerkship. What STFM resources are available to help me design or update clerkship curricula?"
Why this is better: Providing details about your role ("program coordinator") and your goal ("design or update clerkship curricula") gives the chatbot enough context to offer more targeted information.

3. Don't Assume the AI Knows Everything

Provide Necessary Details:The STFM AI Assistant has been trained on STFM's business and resources. The AI can only use the information you provide or that it has been trained on.

Example:

Instead of: "How can I improve my program?"
Try: "As a program coordinator for a Family Medicine clerkship, what resources does STFM provide to help me improve student engagement and learning outcomes?"
Why this is important: Including relevant details helps the AI understand your specific situation, leading to more accurate and useful responses.

4. Reset if You Change Topics

Clear Chat History When Switching Topics:

If you move to a completely new topic and the chatbot doesn't recognize the change, click the Clear Chat History button and restate your question.
Note: Clearing your chat history removes all previous context from the chatbot's memory.
Why this is important: Resetting ensures the AI does not carry over irrelevant information, which could lead to confusion or inaccurate answers.

5. Provide Enough Context

Include Background Information: The more context you provide, the better the chatbot can understand and respond to your question.

Example:

Instead of: "What are the best practices?"
Try: "In the context of Family Medicine education, what are the best practices for integrating clinical simulations into the curriculum?"
Why this is important: Specific goals, constraints, or preferences allow the AI to tailor its responses to your unique needs.

6. Ask One Question at a Time

Break Down Complex Queries: If you have multiple questions, ask them separately.

Example:

Instead of: "What are the requirements for faculty development, how do I register for conferences, and what grants are available?"
Try: Start with "What are the faculty development requirements for Family Medicine educators?" Then follow up with your other questions after receiving the response.
Why this is important: This approach ensures each question gets full attention and a complete answer.

Examples of Good vs. Bad Prompts

Bad Prompt

"What type of membership is best for me?"

Why it's bad: The AI Chat Assistant has no information about your background or needs.

Good Prompt

"I'm the chair of the Department of Family Medicine at a major university, and I plan to retire next year. I'd like to stay involved with Family Medicine education. What type of membership is best for me?"

Why it's good: The AI Chat Assistant knows your role, your future plans, and your interest in staying involved, enabling it to provide more relevant advice.

Double Check Important Information

While the AI Chat Assistant is a helpful tool, it can still produce inaccurate or incomplete responses. Always verify critical information with reliable sources or colleagues before taking action.

Technical Limitations

The Chat Assistant:

  • Cannot access external websites or open links
  • Cannot process or view images
  • Cannot make changes to STFM systems or process transactions
  • Cannot access real-time information (like your STFM Member Profile information)

STFM AI Assistant
Disclaimer: The STFM Assistant can make mistakes. Check important information.