Integrating Artificial Intelligence into Medical Education in LMICs: A Narrative Review

Artificial intelligence (AI) is reshaping clinical practice, yet formal AI education in medical curricula has lagged significantly behind-a gap particularly acute in low- and middle-income countries (LMICs). This narrative review examines AI integration in medical education across LMICs, with primary contextual focus on sub-Saharan Africa and African health systems within this broader framing. Available evidence suggests that a substantial proportion of medical students globally may lack formal AI education despite growing clinical AI adoption among physicians, with LMICs and African contexts disproportionately underrepresented in AI-in-medical-education literature. African contexts face compounding implementation challenges-infrastructure deficits, data scarcity, algorithmic bias in externally designed tools, and regulatory gaps-yet possess distinctive contextual opportunities. Applying a structured critical counterargument analysis, the review interrogates both the rationale for integration and the strongest arguments for delay. The review's contribution lies in its LMICs-and-Africa-centred framing, its integration of three complementary theoretical frameworks, and its policy-oriented, phased implementation synthesis-dimensions not addressed in aggregate by existing reviews. AI integration in medical education in LMICs is a context-sensitive priority. The risks of unplanned inaction-widening competency gaps and forfeiture of iterative evaluation data-should be weighed against the risks of implementation, with careful, locally adapted, phased approaches offering the most defensible pathway forward.