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Considering Biased Data as Informative Artifacts in AI-Assisted Health Care.
Ferryman, Kadija; Mackintosh, Maxine; Ghassemi, Marzyeh.
Afiliação
  • Ferryman K; From the Johns Hopkins Berman Institute of Bioethics and the Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore (K.F.); Genomics England and the Alan Turing Institute, London (M.M.); and the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA (M.G.).
  • Mackintosh M; From the Johns Hopkins Berman Institute of Bioethics and the Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore (K.F.); Genomics England and the Alan Turing Institute, London (M.M.); and the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA (M.G.).
  • Ghassemi M; From the Johns Hopkins Berman Institute of Bioethics and the Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore (K.F.); Genomics England and the Alan Turing Institute, London (M.M.); and the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA (M.G.).
N Engl J Med ; 389(9): 833-838, 2023 Aug 31.
Article em En | MEDLINE | ID: mdl-37646680
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Viés / Interpretação Estatística de Dados / Atenção à Saúde Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Viés / Interpretação Estatística de Dados / Atenção à Saúde Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article