(November 2, 2026) -- 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, PhD, MPH, Georgetown University, and colleagues. The research was published October 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.
The Challenge
“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 healthcare settings.”
The Barriers
“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.”
The Discovery
“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 risk that resources will be primarily allocated to the most advantaged group, rather than the group who needs the resources the most.
The majority (70%) of the publications we analyzed had author teams based in the United States. A US-centric perspective on AI and health equity likely has blind spots. We identified a need for more global perspectives on how to use AI to promote health equity.
We also found other significant outcomes, including:
The Path Toward Solutions
“Diverse, interdisciplinary teams are essential for AI algorithms that are being developed and implemented in healthcare settings. The inclusion of data scientists, health equity-focused researchers, healthcare providers, specialists, and patients has the potential to reduce model bias. To make sure that all individuals can experience optimal healthcare delivery and health outcomes, the use of AI in healthcare research and practice should be guided by health equity principles across all stages of model design, implementation and evaluation.”
Our review underscores the need for more scholarship concerning how AI use relates to the treatment of specific health outcomes and conditions. Specialty-specific expertise (e.g., radiology, dermatology, cardiology) is crucial for reducing the risk of worsening health disparities when AI is used in healthcare delivery. For example, specialists may be better equipped than data scientists to recognize how diagnostic disparities could occur because of the utilization of training data that does not include diverse and representative patient populations.”
The Possibilities
“The use of inclusive research design and methodologies could also help ensure that AI is applied in ways that promote health for all populations.”
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Study title: Recommended guidelines concerning artificial intelligence and health equity: a scoping review.
In addition to Efird, other authors include Claudia Park and Parth Mishra, undergraduate students in the School of Health Department of Health Management and Policy, Georgetown University; Angela Barr, former Georgetown University librarian, currently at West Shore Community College; Andrew Twinamatsiko at the O’Neill Institute for National and Global Health Law, Georgetown University Law Center; and Chrisopher King, Dean of the School of Health, Georgetown University.
Caroline Efird and her co-authors report having no personal financial interests related to the study.
The study was supported in part by the Georgetown University School of Health.
Health Affairs Scholar
Literature review
Not applicable
Recommended guidelines concerning artificial intelligence and health equity: a scoping review
8-Oct-2026