STFM Podcast Bonus Conference Episode

2025 Annual Spring Conference Blanchard Lecture

As a special episode of the STFM Podcast, listen to an audio recording of the 2025 Annual Spring Conference Blanchard Lecture: "Building Equity into Health Care AI: From Promise to Practice" by Irene Dankwa-Mullan, MD, MPH, Dartmouth College.

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Wednesday, May 7, 2025

As the integration of artificial intelligence (AI) in health care accelerates, the promise of improved patient outcomes and operational efficiencies is accompanied by critical concerns about the impact on health equity. Family medicine, with its commitment to holistic, patient-centered care, plays a vital role in ensuring that AI solutions contribute to more equitable health care delivery rather than perpetuating existing disparities.In this keynote presentation, the presenter explores how health care AI can be harnessed to advance equity while also addressing the significant risks posed by biased data and flawed algorithms. Drawing on her work in AI ethics and health equity, Dr Dankwa-Mullan provides a practical framework for the intentional design and deployment of AI tools that promote fairness in patient care. She discusses key strategies for mitigating biases in clinical algorithms, ensuring diverse patient representation in AI training data, and advocating for policies and practices that uphold equity at every stage of AI development.This session empowers health care professionals and educators to actively engage in shaping AI’s future—transforming concerns into action by advocating for responsible AI use, inclusive design processes, and equitable outcomes for all patient populations.

Learning Objectives

Upon completion of this session, participants should be able to:

Identify potential sources of bias in health care AI systems and understand their impact on health equity
Understand the principles of equitable AI design and deployment in clinical settings
Explore strategies for family medicine educators to advocate for the intentional development and use of AI technologies that promote health equity
Develop actionable steps to ensure diverse representation and fairness in data used for health care AI algorithms
Recognize the role of health care professionals in shaping the future of AI to achieve more equitable patient outcomes

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Irene Dankwa-Mullan, MD, MPH

Irene Dankwa-Mullan, MD, MPH, is a leading expert at the intersection of artificial intelligence, health equity, and clinical care, with over a decade of experience driving innovation in the health care industry. She has worked extensively with academic institutions, nonprofit organizations, and health care systems to develop equitable AI frameworks that ensure the fair and inclusive deployment of technology in medicine. Her expertise lies in integrating AI and machine learning tools into clinical workflows while advocating for policies that prioritize diverse patient populations and mitigate systemic biases in data and algorithms.Dr Dankwa-Mullan currently advises on how to evaluate health care delivery systems' readiness to procure, evaluate, and deploy AI solutions that adhere to the highest ethical and technical standards. She also works with early-stage health technology start-up companies, where she leverages her industry experience and expertise to build fair and inclusive AI solutions. She has published widely on the ethical implications of AI in health care and frequently speaks at national and international conferences on the role of AI in advancing health equity. She is passionate about educating the next generation of health care leaders to be advocates for responsible, patient-centered AI. She is committed to ensuring that AI serves as a tool for reducing health disparities, not exacerbating them.

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2025 Annual Spring Conference Blanchard Lecture

Wednesday, May 7, 2025

Learning Objectives

Listen to All Episodes

Irene Dankwa-Mullan, MD, MPH

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AI Chatbot Tips

Tips for Using STFM's AI Assistant

1. Avoid Ambiguous Language

Example:

2. Use Specific Terms

Example:

3. Don't Assume the AI Knows Everything

Example:

4. Reset if You Change Topics

5. Provide Enough Context

Example:

6. Ask One Question at a Time

Example:

Examples of Good vs. Bad Prompts

Bad Prompt

Good Prompt

Double Check Important Information

Technical Limitations

STFM AI Assistant