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March 2025: Artificial Intelligence and Machine Learning for Primary Care - A Panel Discussion

Step into the future of primary care with Artificial Intelligence and Machine Learning (AI/ML). In this episode, you’ll discover how these transformative technologies are revolutionizing healthcare as three expert voices from STFM’s cutting-edge Artificial Intelligence and Machine Learning for Primary Care Curriculum reveal insider strategies to slash administrative burden—and maybe even carve out time for your dream vacation. Whether you’re an educator eager to innovate, or a clinician ready to lead your team in implementing new tools, this dynamic panel delivers practical tips, ethical insights, and the inspiration you need to confidently participate in the AI revolution.

Our Panelists:

  • Cornelius James, MD

  • Jaky Kueper, PhD

  • Winston Liaw, MD, MPH

Hosted by Omari A. Hodge, MD, and Jay-Sheree Allen Akambase, MD
Copyright © Society of Teachers of Family Medicine, 2025

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Cornelius James, MD

Dr. James is an Assistant Professor in the Departments of Internal Medicine, Pediatrics and Learning Health Sciences at the University of Michigan (U-M). He is a general internist and a general pediatrician practicing as a primary care physician. He holds the James O. Woolliscroft, MD Endowment in Humane Patient Care.

Dr. James has served in many educational roles across the continuum of medical education, including serving as the director of the University of Michigan Medical School evidence-based medicine curriculum, and an Associate Program Director for the U-M Internal Medicine Residency Program. He also serves on local and national committees, including the U-M Clinical Intelligence Committee and the International Advisory Committee for Artificial Intelligence. 

In multiple years Dr. James has been identified as one of the top teachers in the Department of Internal Medicine. In addition, in 2022 he received the Kaiser Permanente Excellence in Teaching award, the most prestigious teaching award given by the U-M medical school. 

Dr. James has completed the American Medical Association (AMA) Health Systems Science Scholars program, and he was also one of ten inaugural 2021 National Academy of Medicine Scholars in Diagnostic Excellence. 

His research interests include augmenting clinical reasoning with artificial intelligence, and equitable implementation of safe and effective digital health tools into clinical practice.

His work has been published in JAMA, Annals of Internal Medicine, Academic Medicine, the Journal of General Internal Medicine, Cell Reports, and more. 

Jaky Kueper, PhD

Jaky Kueper, PhD, is an epidemiologist and computer scientist with the Scripps Research Digital Trials Center. Her work in AI for primary care ranges from investigating primary care AI needs and priorities to co-developing AI solutions with Community Health Centres. She's also been engaged in several AI for health capacity building initiatives, including development of the Introduction to AI for Family Medicine e-course at the College of Family Physicians of Canada and the AiM-PC Curriculum with the Society of Teachers of Family Medicine.

 

Winston Liaw, MD, MPH

Winston Liaw is a family physician, health services researcher, and the Chair of Health Systems and Population Health Sciences at the University of Houston Tilman J. Fertitta Family College of Medicine. His research focuses on the use of artificial intelligence in primary care and assessing and addressing unmet social needs within primary care settings. Prior to joining the University of Houston, he was a researcher at the University of Texas Health Science Center at Houston and was the Medical Director at the Robert Graham Center, a primary care policy research institute affiliated with the American Academy of Family Physicians. He also served as residency faculty at the Virginia Commonwealth University, Fairfax Family Medicine Residency Program. Dr. Liaw received a BA degree from Rice University, an MD from Baylor College of Medicine, an MPH from the Harvard School of Public Health, family medicine residency training from Virginia Commonwealth University, and health policy fellowship training from the Robert Graham Center. 

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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.