Data bias in precision medicine

Abstract

Precision medicine is poised to increasingly improve health outcomes for more people in the near future. In contrast to the more traditional reactive methods of disease treatment, precision medicine is a customizable treatment and disease prevention approach that is tailored for the individual. Artificial intelligence (AI) using sophisticated algorithms and machine learning (ML) tools powers these precision medicine processes. These algorithms analyze big data collected from multiple sources over the past decades to aid physicians to make data-backed critical clinical decisions. However, studies have shown that unintentional biases in the source data and in the process can affect these precision medicine efforts.