ReviewAdvances in medical education and practice2026
Integrating Artificial Intelligence into Medical Education in LMICs: A Narrative Review.
Review in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 author.
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Abstract
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.
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