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

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Global Health Equity

Background:

• Formal artificial intelligence (AI) education is not keeping pace with AI's integration into clinical practice.
• This educational gap is particularly pronounced in low- and middle-income countries (LMICs), including sub-Saharan Africa.
• Existing literature underrepresents AI in medical education within LMICs and African contexts.

Purpose of the Study:

• To review the integration of AI in medical education across LMICs, focusing on sub-Saharan Africa.
• To analyze the challenges and opportunities for AI in medical education within African health systems.
• To critically evaluate arguments for and against AI integration in LMIC medical curricula.

Main Methods:

• A narrative review approach was employed.
• Focus on LMICs, with specific attention to sub-Saharan Africa.
• Utilized structured critical counterargument analysis and integrated three theoretical frameworks.

Main Results:

• Many medical students globally lack formal AI education, with LMICs disproportionately affected.
• African contexts face significant implementation hurdles: infrastructure, data scarcity, algorithmic bias, and regulatory issues.
• Distinctive contextual opportunities for AI in medical education exist within Africa.

Conclusions:

• AI integration in LMIC medical education is a context-specific priority.
• Weigh the risks of inaction (widening competency gaps) against implementation risks.
• Locally adapted, phased implementation strategies offer the most viable path forward.