Ensuring health equity with the use of AI
Only 13% of articles examined in a review of artificial intelligence (AI) and health equity explicitly defined health equity—a gap that could leave AI-driven approaches benefiting the most advantaged groups. That's a key finding from a review led by Caroline R. Efird of Georgetown University and colleagues. The research was published Oct. 8, 2026, in Health Affairs Scholar.
A recent review of artificial intelligence and health equity research published in Health Affairs Scholar highlights a significant gap in the definition of health equity. Only 13% of the examined 166 articles explicitly defined health equity, leaving room for AI-driven approaches to potentially benefit only the most privileged groups.
The study, led by Caroline R. Efird of Georgetown University, underscores the need for clear definitions, global perspectives, and diverse interdisciplinary teams to ensure AI promotes health equity. The majority of publications (70%) had U.S.-based author teams, which may overlook global implications. Additionally, only 32% of the studies focused on specific health outcomes or issues, with 68% lacking a specific focus.
To address these gaps, the research suggests incorporating health equity principles at every stage of AI model design, implementation, and evaluation. Specialists in specific health domains, such as radiology or cardiology, could provide valuable insights to mitigate model bias by recognizing diagnostic disparities in training data.
Overall, the review emphasizes the importance of inclusive research design and methodologies to guide the equitable application of AI in health care settings.
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