ArticlePLOS digital health2025
Standardization and accuracy of race and ethnicity data: Equity implications for medical AI.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- A New Playbook: State-Driven Solutions for Resilient Health Data.The Milbank quarterly · 2026Article
- Knowledge Architecture for Race and Ethnic Group Defining in Learning Health Systems.Learning health systems · 2026Article
- Advancing brain health equity with Indigenous peoples: A critical imperative.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare.Healthcare (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The rapid integration of artificial intelligence (AI) into healthcare has raised many concerns about race bias in AI models. Yet, overlooked in this dialogue is the lack of quality control for the accuracy of patient race and ethnicity (r/e) data in electronic health records (EHR). This article critically examines the factors driving inaccurate and unrepresentative r/e datasets. These include conceptual uncertainties about how to categorize races and ethnicity, shortcomings in data collection practices, EHR standards, and the misclassification of patients' race or ethnicity. To address these challenges, we propose a two-pronged action plan. First, we present a set of best practices for healthcare systems and medical AI researchers to improve r/e data accuracy. Second, we call for developers of medical AI models to transparently warrant the quality of their r/e data. Given the ethical and scientific imperatives of ensuring high-quality r/e data in AI-driven healthcare, we argue that these steps should be taken immediately.
Identifiers
What OpenQuestion 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 OpenQuestion graph.