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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.

We asked Efird, assistant professor in the Department of Health Management and Policy in the School of Health at Georgetown University, to share more about her research and its potential implications.

"Algorithmic bias, which can occur when artificial intelligence models produce systematically unfair, discriminatory or disparate outcomes that favor certain groups or individuals over others, is often unintentionally embedded in artificial intelligence (AI) algorithms in ways that can widen health disparities and inequities. The lack of transparency and stewardship concerning AI use is concerning because marginalized populations will experience the greatest health risk. Health disparities could worsen if we do not intentionally regulate how AI is used in health care settings."

"Our research is trying to ensure that health systems, hospitals, clinics and the field of health services research implement AI in ways that will work to promote health rather than unintentionally harm it."

"Only 13% of the 166 articles included in our review had a specific definition of health equity. When health equity is vaguely defined, there is a risk that resources will be primarily allocated to the most advantaged group, rather than the group that needs the resources the most.

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