  As more clinical workflows continue to be augmented by artificial
intelligence (AI), AI literacy among physicians will become a critical
requirement for ensuring safe and ethical AI-enabled patient care. Despite the
evolving importance of AI in healthcare, the extent to which it has been
adopted into traditional and often-overloaded medical curricula is currently
unknown. In a scoping review of 1,699 articles published between January 2016
and June 2024, we identified 18 studies which propose guiding frameworks, and
11 studies documenting real-world instruction, centered around the integration
of AI into medical education. We found that comprehensive guidelines will
require greater clinical relevance and personalization to suit medical student
interests and career trajectories. Current efforts highlight discrepancies in
the teaching guidelines, emphasizing AI evaluation and ethics over technical
topics such as data science and coding. Additionally, we identified several
challenges associated with integrating AI training into the medical education
program, including a lack of guidelines to define medical students AI literacy,
a perceived lack of proven clinical value, and a scarcity of qualified
instructors. With this knowledge, we propose an AI literacy framework to define
competencies for medical students. To prioritize relevant and personalized AI
education, we categorize literacy into four dimensions: Foundational,
Practical, Experimental, and Ethical, with tailored learning objectives to the
pre-clinical, clinical, and clinical research stages of medical education. This
review provides a road map for developing practical and relevant education
strategies for building an AI-competent healthcare workforce.
