Publication Title
PLOS Digital Health
Volume
4
Page
0000807
Year
2025
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.
Recommended Citation
Alexandra Tsalidis, Lakshmi Bharadwaj, and Francis X. Shen, Standardization and Accuracy of Race and Ethnicity Data: Equity Implications for Medical AI, 4 PLOS Digital Health 0000807 (2025), available at https://scholarship.law.umn.edu/faculty_articles/1135.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
