Differences in Expert Perspectives on AI Training in Medical Education: Secondary Analysis of a Multinational Delphi...

Area of Science:

• Medical Education
• Artificial Intelligence
• Global Health Equity

Background:

• Artificial intelligence (AI) is increasingly integrated into healthcare, necessitating AI literacy among medical professionals.
• Existing medical education curricula may not adequately address AI competencies, particularly in resource-limited settings.
• Global disparities in access to technology and training exacerbate inequalities in AI education.

Purpose of the Study:

• To analyze global expert opinions on the mandatory inclusion of artificial intelligence (AI) learning outcomes in preregistration medical education.
• To identify differences in these opinions between experts from high-income countries (HICs) and low- and middle-income countries (LMICs).
• To explore the implications of these differences for developing equitable AI competency frameworks in medical training.

Main Methods:

• Secondary analysis of a multinational Delphi study involving medical education experts.
• Comparative analysis of expert recommendations regarding AI learning outcomes based on country income levels (HIC vs. LMIC).
• Qualitative assessment of factors influencing expert opinions on AI integration in medical curricula.

Main Results:

• Experts from LMICs were significantly less likely than those from HICs to consider AI learning outcomes mandatory in preregistration medical education.
• This trend suggests a potential gap in the perceived importance or feasibility of AI education in resource-constrained environments.
• The findings underscore variations in the adoption and prioritization of AI in medical training globally.

Conclusions:

• Global inequalities in medical AI education are evident, with LMIC experts showing less emphasis on mandatory AI learning outcomes.
• There is a critical need for adaptable and inclusive AI competency frameworks to address these disparities.
• Future efforts should focus on equitable AI training strategies to ensure all healthcare professionals are prepared for an AI-driven future.