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Artificial Intelligence (AI) applications in health care continue to grow exponentially, and AI-tools such as machine learning (ML), natural language processing (NLP), and generative pre-trained transformers (GPT) continue to transform medical education. The adoption of AI in medical education however remains geographically varied, with studies illustrating a relatively lower research output on medical AI literacy in Latin America (LATAM) countries compared to North America. AI-applications offer significant potential to broaden access to medical education and facilitate evidence-based support with clinical decision-making, particularly in low- and middle-income countries (LMICs). Enhancing AI literacy in such settings is of paramount importance - successful integration may alleviate historical disparities, while improper implementation may deepen regional inequities in medical education and health care. In this viewpoint article, we illustrate the current state of AI-based medical education across the Americas, highlight challenges in implementation, and offer collaborative, equitable, and regionally tailored strategies to enhance medical AI literacy among health care professionals and students.
Patel et al. (2026) studied this question.