Artificial intelligence’s (AI) rapid advances in health care have intensified calls for incorporating AI training into medical education [1]. However, few existing AI-related medical curricula are tailored to specific national contexts and lack global applicability [2,3]. Perspectives on the relevance and prioritization of AI training may vary between high-income countries (HICs) and low- and middle-income countries (LMICs), as do the development, evaluation, and implementation of health care AI technologies [4,5]. Understanding differences in AI training priorities is crucial for designing medical curricula addressing global and national health care needs. We examined differences in perspectives on the prioritization of AI learning outcomes (LOs) in preregistration medical education between experts from HICs and LMICs.

Of the 149 experts who participated in both rounds of the original Delphi study, 130 (87.2%) completed the AI LO section (n=59, 45.4% from HICs and n=71, 54.6% from LMICs). Significant differences between HIC and LMIC participants were observed in teaching roles (P=.01), clinical setting (hospital or private practice; P=.03), or institutional leadership positions (P<.001; Table 1). In the adjusted model, experts from LMICs were significantly less likely to rate AI LOs as mandatory in medical education (odds ratio 0.58, 95% CI 0.37‐0.91; P=.02) compared to their HIC counterparts (Table 2).

In this secondary analysis of data from 130 experts, those from LMICs appeared less likely to consider AI LOs as mandatory compared to their HIC counterparts, which may have implications for widening inequalities in medical AI expertise between HICs and LMICs. Our findings extend research on regional variation in attitudes toward AI in health care. One cross-sectional study revealed differing views on AI in practice between health profession students from the Global North and Global South [8], while another study reported lower confidence in clinical AI among physicians and medical students from lower middle– and low-income countries compared to those from upper middle– and high-income countries [9]. The congruence of our findings with these studies points to a potential divergence in AI perceptions across income settings, likely reflecting broader structural inequalities such as disparities in digital infrastructure, educational resources, institutional readiness, and exposure to AI technologies, rather than disagreement on the value of AI training.

Several limitations should be noted. Binary classification of countries may mask important intragroup differences. Imbalanced representation of participant roles may have introduced bias, and important demographic variables such as age, gender, and years of experience were not available. Expert migration, institutional policies, and exposure to AI may also have influenced expert responses. Future research should use qualitative methods to elucidate contextual determinants of expert opinions on AI training.

In conclusion, our findings indicate that a one-size-fits-all approach to AI training in medical education may not be appropriate. An adaptable, needs-based framework that considers socioeconomic, infrastructural, and health care disparities could better serve the diverse needs of medical students and health systems worldwide.