Bias-Mitigated AI as a Foundation for Resilient and Effective Health Systems

Artificial intelligence (AI) is rapidly reshaping the landscape of health care, from clinical diagnostics and disease surveillance to the prediction of individual health risks. Yet, its immense promise will only materialize if the tools we deploy work for everyone. Algorithms trained on incomplete or biased datasets risk embedding historical health disparities and can replicate patterns of uneven data representation, thereby limiting accuracy and generalizability across population groups. Addressing algorithmic bias should be treated as a core health quality standard, comparable in importance to safety and efficacy evaluations, to ensure consistent performance across all segments of the population.