ReviewHealth affairs scholar2026
Recommended guidelines concerning artificial intelligence and health equity: a scoping review.
Review in Health affairs scholar, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The use of artificial intelligence (AI) technologies in healthcare has potential to improve patient care, expand access, and optimize efficiency. Yet, a growing body of evidence warns of health risks for marginalized and minoritized populations as a result of algorithmic bias. Methods: To synthesize leading scholars' recommendations for equitable AI implementation in healthcare settings and health services research, we conducted a scoping review of current literature. Results: We identified and analyzed 166 relevant, peer-reviewed articles. Most articles (70%) originated in the United States. Only 13% provided a specific definition of "health equity." Included manuscripts generally emphasized that ethical human oversight must accompany AI-use. The engagement of diverse groups of stakeholders during model development, implementation, and evaluation is also paramount. Patient and provider education on AI's capabilities is also critical for building trust and equitable delivery of care. Conclusion: To enhance the field's ability to implement AI in ways that address health disparities and inequities, future research needs to amplify discipline-specific expertise and more global perspectives. By explicitly prioritizing health equity, transparency, inclusion, and education, there is potential to meaningfully regulate AI in healthcare to redress systemic inequities in care and advance health and well-being in populations throughout the world.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.