Post

The use of race in clinical risk models is heavily debated. While race-aware models can be more accurate, some are concerned about reinforcing racialized views of medicine. In our paper, we offer a new perspective on this debate. 🧵👇https://annals.org/aim/article/doi/10.7326/M23-3166

Using cardiovascular disease, breast cancer, and lung cancer as case studies, we show that race-unaware models are often miscalibrated—underestimating risk for some groups and overestimating it for others. This finding is consistent with evidence cited in support of the use of race-aware models.

Yet, despite this miscalibration, clinical decisions (e.g., screening or treatment recommendations) differ between race-aware and race-unaware models for only a small fraction of individuals (~5%). The individuals whose decisions flip are those closest to the decision threshold.

As a result, the overall clinical utility of race-aware models is surprisingly small. Context matters, but the benefits of race-aware models have likely been overstated.

Our study comes with several important caveats. Notably, in resource-constrained settings (e.g. organ transplants), race-aware models are expected to offer more substantial utility gains.

We hope that our work underscores the importance of foregrounding not only improvements in accuracy, but changes in *decisions and utility* in considering the use of race and ethnicity clinical decision-making.