JMIR Publications

ABSTRACT

Artificial intelligence (AI) is rapidly reshaping the landscape of health, 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. When algorithms are trained on incomplete or biased datasets, they risk embedding historical health disparities and can replicate patterns of uneven data representation that limit accuracy and generalizability across population groups (1). Addressing algorithmic bias should be treated as a health quality standard, comparable in importance to safety and efficacy evaluations, ensuring consistent performance across all segments of the population. This editorial aims to inform both policymakers and technical experts, offering a framework that bridges scientific rigor with practical, regionally grounded governance models.