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    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. Clear documentation should outline when, where, and how AI is used in education and clinical learning environment contexts to uphold integrity and foster trust. Disclosure of AI use should be foundational, meaning learners, educators, and staff alike should appropriately communicate where and how AI is being used. This encompasses adhering to rigorous ethical standards for data acquisition and use, safeguarding privacy, and systematically identifying and mitigating biases. To ensure fairness, strategies should be actively developed and deployed to monitor and address biases, thus promoting equitable outcomes for all stakeholders. Furthermore, AI integration should align with established standards and guidelines by regulatory authorities, when possible. Intellectual property concerns related to AI-generated content should also be addressed, emphasizing compliance with copyright laws and ownership rights to avoid infringement.

    From Principle to Practice

    Apply this principle to your practice using the following strategies: 

    • Promote transparency. Develop and disseminate clear protocols for AI interaction in medical education, explicitly outlining permissible and encouraged behaviors for educators, staff, and learners. Establish a standardized protocol to document AI integration in medical education, clearly outlining when, where, and how AI tools are used. This protocol should detail the specific tools employed, their intended purposes, data-handling practices, and underlying algorithmic processes. All documentation should be readily accessible to stakeholders — schools, programs, educators, staff, and learners. Implementation should include communication channels for updates, a system for reporting concerns, and comprehensive mechanisms to identify and address implementation inequities, ethical considerations, and their impacts on teaching and learning.
    • Establish ethical guardrails and oversight. Establish specific guidelines regarding data usage, including what types of information can and cannot be shared with AI systems. Address intellectual property considerations by creating clear policies on the use and attribution of AI-generated content, ensuring compliance with copyright laws, and protecting ownership rights. Regularly update and communicate these guidelines to maintain alignment with evolving AI capabilities and institutional needs. Invite existing ethicists and ethics committees to advise on the design, development, and deployment of AI tools being considered for use in medical education to help ensure adherence to ethical guidelines and regulatory standards. Implement comprehensive privacy policies that comply with the Family Educational Rights and Privacy Act (FERPA), General Data Protection Regulation, and other relevant regulations, including explicit consent mechanisms for data collection and usage. Develop systematic processes for identifying and mitigating algorithmic biases through regular data audits and diverse dataset creation. Periodic reviews of AI applications addressing issues of transparency, accountability, and privacy while monitoring the effectiveness of bias mitigation strategies should be conducted and policies updated as needed.
    • Educate on ethical and responsible use. Develop comprehensive education and training programs for all stakeholders on the ethical implications and responsible use of AI in medical education. Include key topics such as AI literacy, data privacy, informed consent, transparency, and intellectual property rights. Ensure educators, staff, and learners understand relevant regulatory frameworks and can identify both benefits and potential ethical pitfalls of AI implementation. Foster an environment that promotes critical thinking and responsible AI adoption through hands-on education and training (e.g., inputting this document into an AI product to use as context in providing recommendations to use AI), regular updates on emerging ethical considerations, and opportunities for stakeholders to actively participate in shaping AI integration practices.

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