Accuracy and Equity in Clinical Risk Prediction

This perspective article explores the intersection of accuracy and equity in clinical risk prediction. Drawing from personal experience and professional work in computer science and population health, the author argues that while efforts to promote fairness in predictive algorithms are crucial, they must not compromise accuracy. Methods such as algorithmic "debiasing" or the categorical removal of race as a predictive factor can unintentionally worsen both accuracy and equity. Instead, risk prediction should balance fairness with precision by addressing context-specific inequities, improving data collection (e.g., genetic ancestry and social determinants of health), and ensuring diverse patient populations in model training. Ultimately, the piece emphasizes the ethical responsibility of clinicians and data scientists to provide patients with the most accurate risk estimates possible, enabling informed life-and-death health decisions.

This perspective article explores the intersection of accuracy and equity in clinical risk prediction. Drawing from personal experience and professional work in computer science and population health, the author argues that while efforts to promote fairness in predictive algorithms are crucial, they must not compromise accuracy. Methods such as algorithmic "debiasing" or the categorical removal of race as a predictive factor can unintentionally worsen both accuracy and equity. Instead, risk prediction should balance fairness with precision by addressing context-specific inequities, improving data collection (e.g., genetic ancestry and social determinants of health), and ensuring diverse patient populations in model training. Ultimately, the piece emphasizes the ethical responsibility of clinicians and data scientists to provide patients with the most accurate risk estimates possible, enabling informed life-and-death health decisions.