What happened
Researchers at Georgetown University examined 166 articles published between 2018 and early 2025, including commentaries, editorials, literature reviews, and other publications providing guidance on equitable AI use in healthcare delivery, health systems, and health services research. They found that only 22 articles explicitly defined health equity, and most of those definitions followed the World Health Organization's description of health equity as the absence of unfair, avoidable, or remediable differences between population groups.
The researchers searched four academic databases in January 2025 and found that just 22 articles explicitly defined health equity.
Most of those definitions followed the World Health Organization's description of health equity as the absence of unfair, avoidable, or remediable differences between population groups.
The review also identified substantial geographic differences in the research, with first authors affiliated with US institutions accounting for 116 articles, or 70% of the total.
The researchers argued that more condition-specific expertise is needed because AI can reproduce disparities already present in medical evidence.
They recommended involving radiologists, oncologists, gynaecologists, neurologists, and other clinicians alongside programmers and health equity specialists throughout model design, development, implementation, and evaluation.
Why it matters
The study highlights the need for more condition-specific expertise in AI development to reduce the risk of worsening health disparities. It also emphasizes the importance of involving clinicians alongside programmers and health equity specialists throughout model design, development, implementation, and evaluation. The researchers recommended that healthcare organisations establish equity-focused programmes with continuous oversight and that national regulators incorporate frameworks based on the WHO definition of health equity into their evaluations.
The study highlights the need for more condition-specific expertise in AI development to reduce the risk of worsening health disparities.
It also emphasizes the importance of involving clinicians alongside programmers and health equity specialists throughout model design, development, implementation, and evaluation.
The researchers recommended that healthcare organisations establish equity-focused programmes with continuous oversight and that national regulators incorporate frameworks based on the WHO definition of health equity into their evaluations.
They also urged researchers to report results for population subgroups and marginalised communities rather than relying exclusively on aggregated performance measures.
Such reporting could help identify circumstances in which average improvements conceal deteriorating outcomes for particular patients.
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What to watch next
The study's findings have significant implications for the development and deployment of AI in healthcare. It highlights the need for greater transparency about how AI is used in healthcare decisions and patient communications, particularly for applications affecting privacy, safety, or legal rights. Patient involvement during development may help identify problems that would otherwise become apparent only after deployment.
The study's findings have significant implications for the development and deployment of AI in healthcare.
It highlights the need for greater transparency about how AI is used in healthcare decisions and patient communications, particularly for applications affecting privacy, safety, or legal rights.
Patient involvement during development may help identify problems that would otherwise become apparent only after deployment.
The evidence from predictive algorithms, clinical decision-support experiments, and disability research indicates that healthcare AI requires evaluation beyond aggregate technical accuracy.
Its effects also depend on the outcomes selected for prediction, the populations represented in training data, and the clinical decisions made using its recommendations.