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

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

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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 sha...

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Bibliographic Details
Main Authors: Kenneth P. Seastedt, Patrick Schwab, Zach O’Brien, Edith Wakida, Karen Herrera, Portia Grace F. Marcelo, Louis Agha-Mir-Salim, Xavier Borrat Frigola, Emily Boardman Ndulue, Alvin Marcelo, Leo Anthony Celi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2022-10-01
Series:PLOS Digital Health
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931202/?tool=EBI

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931202/?tool=EBI

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