Abstract
Abstract Artificial intelligence (AI) is rapidly transforming medical education by enabling personalized learning, adaptive assessment, and simulation-based training. While high-income countries have begun integrating AI into undergraduate medical curricula, its adoption in low- and middle-income countries (LMICs) remains limited due to infrastructural, financial, and regulatory constraints. This narrative review synthesizes recent literature on the role of AI in undergraduate medical education, examining its applications in curriculum design, teaching methodologies, student learning support, assessment strategies, and educational tool development. It also explores faculty and student perspectives, alongside ethical, technological, and pedagogical challenges, with a particular focus on LMIC contexts. The findings suggest that AI can enhance learning efficiency, engagement, and assessment practices; however, its impact is highly context-dependent and influenced by implementation strategies. Key barriers include limited infrastructure, lack of faculty preparedness, and concerns related to bias, data privacy, and overreliance. Effective integration requires investment in digital infrastructure, faculty development, ethical governance, and locally contextualized AI solutions. AI should therefore be viewed as a complementary tool that augments, rather than replace, human educators, supporting more equitable and adaptive medical education systems.