Applicability and integration of Artificial Intelligence in medical education in Latin America: A Systematic Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> The integration of artificial intelligence (AI) into medical education has transformed teaching and learning paradigms globally. While high-income regions have advanced in curricular adoption of AI tools, their applicability in Latin America remains underexplored and fragmented, raising concerns about equity, pedagogical alignment, and ethical implementation. </sec> <sec> <title>OBJECTIVE</title> To evaluate the current use, effectiveness, and pedagogical foundations of AI tools in medical education across Latin America, identifying implementation patterns, perceived benefits, and key barriers. </sec> <sec> <title>METHODS</title> A systematic review was conducted in accordance with PRISMA guidelines and structured using the CoCoPop and PEO frameworks. An extensive search of six major databases and regional scientific libraries retrieved 284 studies. After applying predefined inclusion and exclusion criteria, 12 primary studies published between 2021 and 2024 were included. Data were extracted and synthesized into descriptive matrices and thematic categories. </sec> <sec> <title>RESULTS</title> Studies originated from eight countries, predominantly using descriptive or quasi-experimental designs. AI tools were applied across three domains: generative content support (e.g., ChatGPT), intelligent tutoring systems, and gamified learning platforms. Most studies reported positive effects on academic performance, knowledge retention, and student motivation. However, fewer than half explicitly stated a pedagogical theory, and none used standardized outcome metrics. Perceptions of AI were generally favorable, yet challenges such as digital inequality, faculty unpreparedness, and ethical concerns were recurrent. </sec> <sec> <title>CONCLUSIONS</title> AI adoption in Latin American medical education is growing but remains in a nascent, exploratory phase. Advancing toward equitable and effective integration will require theory-driven research, faculty training, and institutional policy frameworks supporting responsible AI use in diverse educational ecosystems. </sec>

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