Bridging the Implementation Gap in Medical AI Education: 3-Lever Framework for Concurrent Reform.

Unlabelled: The rapid integration of artificial intelligence (AI) into clinical practice necessitates urgent restructuring of medical education and physician assessment to ensure that future physicians are proficient and responsible users of AI tools. Despite the existence of core AI competencies, the current state of AI education in Canadian undergraduate medical education is highly inconsistent and disjointed, and available data indicate that most medical students receive minimal to no formal AI training even as they anticipate that AI will profoundly shape their future careers. National policy, specifically the Pan-Canadian AI for Health Guiding Principles, has advanced the agenda by calling for AI literacy among health professionals and emphasizing core values such as equity, robust data practices, and Indigenous-led data governance. However, these principles offer limited practical guidance on the educational and regulatory mechanisms required for effective implementation. We contend that this critical implementation deficit arises from a traditional, sequential reform model in which faculty development, curriculum change, and regulatory updates occur in isolation. This slow, siloed approach is fundamentally inadequate for addressing AI's inherent speed, opacity, and significant equity risks. To overcome this challenge, we propose a 3-lever concurrent implementation framework that provides a conceptual lens to address the interdependencies among faculty development, curriculum, and regulation. This model posits that AI competencies transition from abstract requirements to practical application only when 3 levers-clinician-educator capacity, digitally enabled learning environments, and regulatory and assessment reform-are activated simultaneously and in alignment. This Viewpoint extends existing AI competency frameworks by theorizing AI curriculum implementation as a problem of concurrent lever activation and by outlining minimum concurrent actions for deans and regulators that can be adapted to competency-based medical education systems. Although illustrated with Canadian examples, the framework is designed to be transferable beyond Canada and offers a testable, licensure-level blueprint for embedding AI competence in medical education.