JMIR Publications

ABSTRACT

Artificial intelligence (AI), particularly generative AI, is rapidly transforming medical education by driving advancements in adaptive learning, clinical simulation, and comprehensive literature synthesis. In Latin America, these technologies serve as a vital incentive for educational innovation, offering a unique opportunity to give access to high-quality training materials in resource-constrained settings. However, the responsible integration of AI extends far beyond mere student adoption or technological efficiency. It demands a paradigm shift for medical educators, who must now actively master these tools to supervise their application, reimagine assessment methods, and safeguard academic integrity, all while ensuring that fundamental clinical reasoning remains at the core of medical training. To inform this Perspective, we conducted a targeted evidence search across PubMed, SciELO, and LILACS on June 30, 2026, evaluating empirical studies on AI utilization, readiness, and curricular integration among Latin American medical educators. Strikingly, the search revealed a profound gap in regional data, yielding only a single relevant study focused on medical specialty professors in Mexico. This scarcity reveals that the educators who must guide AI adoption remain critically underrepresented in the literature. We introduce a framework for AI integration in Latin American medical education. Our approach begins by identifying faculty readiness and sequentially builds competence in AI literacy, pedagogical application, and assessment redesign, all while rigorously upholding academic integrity and equity. Most importantly, this framework positions medical educators as critical mediators of human–AI interaction, preserving human agency, clinical reasoning, and academic integrity while enabling responsible AI-supported learning. The future of AI in our region depends no longer on monitoring student adoption, but rather on empowering educators and institutions to champion its responsible integration.