Global healthcare fairness: We should be sharing more, not less, data.

ABSTRACT: The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost-measured in terms of access to future medical innovations and clinical software-of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great

SUBMITTER: Seastedt KP 

PROVIDER: S-EPMC9931202 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature