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    Principles for the Responsible Use of Artificial Intelligence in and for Medical Education

    Artificial intelligence (AI) refers to a broad range of advanced techniques and processes that perform complex tasks, such as large language models, machine learning, and natural language processing. As the existing literature indicates, AI holds great promise for medicine, and there is an urgent call to action to integrate and use AI in education and training. Doing so will enable the future workforce to leverage AI in practice and will equip them with the skills to adapt to emerging technologies in the service of high-quality patient care.

    As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI’s power while ensuring the safety and well-being of humanity.1

    A Focus on Medical Education Principles

    The AAMC has developed a framework to support integrating AI into medical education. This framework examines AI’s transformative potential through two essential pillars:

    • AI in medical education: supporting learners along their developmental continuum to responsibly integrate AI into practice. This pillar focuses on threading AI into the curriculum to prepare learners for the use of AI in the delivery of high-quality health care and to ensure educators and staff are appropriately prepared to teach and facilitate learning of AI-enabled, patient-centered care.
    • AI for medical education: building and incorporating AI into our tasks, processes, and systems. This pillar focuses on how AI is used to optimize the medical education process for learners, including using AI to improve assessment of learning outcomes and educational effectiveness while maintaining a commitment to equity and ethical considerations (e.g., Principles for Responsible AI in Medical School and Residency Selection).

    The medical education community should consider how to apply AI-based technology and systems to established teaching and learning practices and, as appropriate, address entirely new issues related to AI with the ultimate focus on advancing education and training. This should occur and be framed at multiple levels: micro (individual), meso (institution), and macro (community). 

    Acknowledging that medical education is in a state of change, the AAMC offers seven key principles to support this time of transition. These principles are meant to be foundational yet flexible for local settings as each institution is unique, with its own mission, culture, and curriculum. An overarching theme of this work is an ongoing commitment to equity and ethics in the use of AI.

    1. Maintain Human-Centered Focus
      As AI technologies advance, human judgment remains essential in determining the appropriate use and implementation of these tools. Medical educators, staff, and learners must apply their critical thinking, creativity, and adaptability to effectively integrate AI into educational practices while maintaining a human-centered approach.
    2. Ensure Ethical and Transparent Use
      The integration of AI into medical education should (a) prioritize responsible use and transparency, ensuring learners and educators receive appropriate disclosures, and (b) equip trainees with the skills to effectively communicate technology use to patients.
    3. Provide Equitable Access to AI
      AI should be used in a way that promotes equity and inclusivity in medical education. All learners need equal opportunities to realize the benefits of these tools in their education, and similarly, institutional variability in access to tools and differing resources to develop capabilities should be addressed. Institutions need to invest in the appropriate infrastructure and collaborate with other institutions to let AI flourish.
    4. Foster Education, Training, and Continuing Professional Development
      Investing in the education, training, and development of educators is essential to prepare them for the growing role of AI in medicine and to help them guide learners through this transformation. Creating a safe AI environment in which educators can explore its use is critical.
    5. Develop Curricula Through Interdisciplinary Collaboration
      Institutions should develop, assess, and implement AI curricula through interdisciplinary collaboration, bringing together experts from medical education, computer science, ethics, sociology, and other relevant fields.
    6. Protect Data Privacy
      Attention to data privacy is critical across the myriad uses of AI within medical education. Specifically, data privacy should be considered within all contexts, including admissions; classroom-, lab-, and workplace-based teaching and learning; coaching and advising; simulation and technology-based experiences; learner assessment; and program evaluation.
    7. Monitor and Evaluate
      AI tools should be frequently evaluated within their intended place of use, whether in the workplace or at the educational program level. Evaluations should guide the implementation of these tools by providing recommendations to learners, educators, and stakeholders. Fostering scholarship to advance AI in the curriculum is critical.

    To learn more about each of these principles, including strategies to apply them to your setting, use the drop-down menu on this page. 

    Note: Because of the dynamic nature of AI in the field, the AAMC will be reviewing and updating these principles every six months. Submit your feedback to be considered for future updates.

    An initial draft of these principles was generated by ChatGPT 4.0 on September 19, 2024. The final draft of version 1.0 of these principles was completed on January 3, 2025.

    Learn how the AAMC is bringing together the academic medicine community and sharing best practices to ensure all are equipped to respond to this important technological advancement.

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    References
    1. Knopp MI, Warm EJ, Weber D, et al. AI-enabled medical education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ. 2023;9:e50373. doi:10.2196/50373 Back to text ↑