%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e50708
%T Perspectives of Black, Latinx, Indigenous, and Asian Communities on Health Data Use and AI: Cross-Sectional Survey Study
%A Rinderknecht,Fatuma-Ayaan
%A Yang,Vivian B
%A Tilahun,Mekaleya
%A Lester,Jenna C
%+ Department of Dermatology, University of California, San Francisco, 1701 Divisadero St, San Francisco, CA, 94115, United States, 1 (415) 353 7800, jenna.lester@ucsf.edu
%K augmented intelligence
%K artificial intelligence
%K health equity
%K dermatology
%K Black
%K Latinx
%K Indigenous
%K Asian
%K racial and ethnic minority communities
%K AI
%K health care
%K health data
%K survey
%K racism
%K large language model
%K LLM
%K diversity
%D 2025
%7 21.2.2025
%9 Research Letter
%J J Med Internet Res
%G English
%X Despite excitement around artificial intelligence (AI)–based tools in health care, there is work to be done before they can be equitably deployed. The absence of diverse patient voices in discussions on AI is a pressing matter, and current studies have been limited in diversity. Our study inquired about the perspectives of racial and ethnic minority patients on the use of their health data in AI, by conducting a cross-sectional survey among 230 participants who were at least 18 years of age and identified as Black, Latinx, Indigenous, or Asian. While familiarity with AI was high, a smaller proportion of participants understood how AI can be used in health care (152/199, 76.4%), and an even smaller proportion understood how AI can be applied to dermatology (133/199, 66.8%). Overall, 69.8% (139/199) of participants agreed that they trusted the health care system to treat their medical information with respect; however, this varied significantly by income (P=.045). Only 64.3% (128/199) of participants felt comfortable with their medical data being used to build AI tools, and 83.4% (166/199) believed they should be compensated if their data are used to develop AI. To our knowledge, this is the first study focused on understanding opinions about health data use for AI among racial and ethnic minority individuals, as similar studies have had limited diversity. It is important to capture the opinions of diverse groups because the inclusion of their data is essential for building equitable AI tools; however, historical harms have made inclusion challenging.
%R 10.2196/50708
%U https://www.jmir.org/2025/1/e50708
%U https://doi.org/10.2196/50708