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

      Seastedt, Kenneth P.; Schwab, Patrick; O’Brien, Zach; Wakida, Edith; Herrera, Karen; Marcelo, Portia Grace F.; Agha-Mir-Salim, Louis; Frigola, Xavier Borrat; Ndulue, Emily Boardman; Marcelo, Alvin; Celi, Leo Anthony (PLOS Digital Health, 2022)
      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 ...