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. It is already being integrated and used in education and training with the goal of enabling the future workforce to leverage AI in practice and equipping 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 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.

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

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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 learners with the skills to effectively communicate technology use to patients.
3. Provide Equal Access to AI
   All learners need equal opportunities to realize the benefits of AI tools in their education. 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, implement, and assess AI curricula through interdisciplinary collaboration, bringing together experts from medical education, computer science, ethics, sociology, and other relevant fields

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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, these guidelines will be updated as needed. 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 was completed on January 3, 2025. Version 2.0 was completed on July 31, 2025.