Ensuring health equity with the use of AI

by Georgetown University Medical Center

edited by Gaby Clark, reviewed by Andrew Zinin

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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.

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 health care 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 a risk that resources will be primarily allocated to the most advantaged group, rather than the group that needs the resources the most.

"The majority (70%) of the publications we analyzed had author teams based in the United States. A U.S.-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:

• Almost 30% of the articles provided a figure, framework or chart to depict and to help facilitate equitable uses of AI in health research and practice.
• Most (68%) manuscripts focused generally on health equity and AI without narrowing in on a specific health outcome or health issue.
• When authors did note specific health outcomes, they included cardiovascular conditions (5%), chronic diseases in general (5%), dermatology and/or skin cancers (5%), COVID-19 (3%), aging (2%), diabetes mellitus (2%) and radiology (2%), among others."

The path toward solutions

"Diverse, interdisciplinary teams are essential for AI algorithms that are being developed and implemented in health care settings. The inclusion of data scientists, health equity-focused researchers, health care providers, specialists and patients has the potential to reduce model bias. To make sure that all individuals can experience optimal health care delivery and health outcomes, the use of AI in health care 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 health care 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."

More information

Caroline R Efird et al., Recommended guidelines concerning artificial intelligence and health equity: a scoping review, Health Affairs Scholar (2026). DOI: 10.1093/haschl/qxag236

Key medical concepts

Health EquityArtificial IntelligenceHealth Disparities

Clinical categories

Preventive medicineCommon illnesses & Prevention Provided by Georgetown University Medical Center Who's behind this story?

Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →

Andrew Zinin

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

Citation: Ensuring health equity with the use of AI (2026, November 2) retrieved 2 November 2026 from https://medicalxpress.com/news/2026-11-health-equity-ai.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.