Accuracy and Equity in Clinical Risk Prediction & Centering Women of Color to Promote Excellence in Academic Medicine

This dual-perspective entry explores structural inequities in healthcare and academia. The first article, authored by a computer scientist and patient, highlights the tradeoffs between algorithmic accuracy and equity in clinical risk prediction. It critiques simplistic approaches to debiasing and calls for nuanced, context-specific models. The second article urges academic medicine to address the marginalization of women of color, especially Black women, in leadership, compensation, and institutional support. In the wake of the rollback of affirmative action, the authors argue for intentional, intersectional strategies to promote equity and excellence. Together, these essays spotlight the imperative of centering both justice and performance in health and academic systems.

This dual-perspective entry explores structural inequities in healthcare and academia. The first article, authored by a computer scientist and patient, highlights the tradeoffs between algorithmic accuracy and equity in clinical risk prediction. It critiques simplistic approaches to debiasing and calls for nuanced, context-specific models. The second article urges academic medicine to address the marginalization of women of color, especially Black women, in leadership, compensation, and institutional support. In the wake of the rollback of affirmative action, the authors argue for intentional, intersectional strategies to promote equity and excellence. Together, these essays spotlight the imperative of centering both justice and performance in health and academic systems.