 The study aims to explore health care education experts' ideas and plans for preparing the health workforce to work with artificial intelligence, AI, and identify critical gaps in curriculum and educational resources across a national health care system. The results highlight the importance of education on ethical implications, suitability of large datasets for use in AI clinical applications, principles of machine learning, and specific diagnosis and treatment applications of AI as well as alterations to cognitive load during clinical work and the interaction between humans and machines in clinical settings. Respondents also outlined barriers to implementation, such as lack of governance structures and processes, resource constraints, and cultural adjustment. This article was authored by Kathleen Gray, John Slavittanec, Gerardo Luis de Maguila, and others.