AI models in primary care can help identify quickly and accurately the people who should receive care, but they risk perpetuating or amplifying biases against diverse groups. This scoping review looked at the strategies and methods used to reduce bias in primary care AI models, which groups were considered, and how these strategies affected both bias and model performance.
Of the 17 studies included, race or ethnicity was the attribute most often examined (12 studies), followed by sex or gender (10 studies), usually defined only as male or female. The approaches to reducing bias fell into four groups: modifying existing models or datasets, sourcing data from electronic health records, developing tools with human input (“human-in-the-loop”), and identifying ethical principles to guide decisions. Adjusting the data before training the model, for example by relabelling or reweighting it, and analysing doctors’ written notes with language-processing tools showed the greatest potential. Other methods sometimes increased errors or made the models less well calibrated.
The authors conclude that bias appears easier to reduce when data are open source, when multiple stakeholders are involved, and when bias is addressed before the model is trained. They call for further studies that include a wider range of groups, such as Indigenous peoples in Canada.
Learn more about this review here: https://doi.org/10.2196/60269
Reference:
Sasseville, M., Ouellet, S., Rhéaume, C., Sahlia, M., Couture, V., Després, P., Paquette, J.-S., Darmon, D., Bergeron, F., & Gagnon, M.-P. (2025). Bias mitigation in primary health care artificial intelligence models: Scoping review. Journal of Medical Internet Research, 27, e60269.