March 27, 2025

Many countries around the world do not collect race and ethnicity data in clinical settings. Without such identified data, it is difficult to identify biases in the training data or output of a given artificial intelligence algorithm, and to work towards medical AI tools that do not exclude or further harm marginalized groups. However, the collection of these data also poses specific risks to marginalized groups. This article weighs the risks of collecting race and ethnicity data in clinical settings against the risks of not collecting those data. Careful navigation of identified data collection is key to building better AI algorithms and to work towards medicine that does not exclude or harm marginalized groups.

Full article: https://doi.org/10.1016/j.landig.2025.01.003