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Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.
Vaid, Akhil; Sawant, Ashwin; Suarez-Farinas, Mayte; Lee, Juhee; Kaul, Sanjeev; Kovatch, Patricia; Freeman, Robert; Jiang, Joy; Jayaraman, Pushkala; Fayad, Zahi; Argulian, Edgar; Lerakis, Stamatios; Charney, Alexander W; Wang, Fei; Levin, Matthew; Glicksberg, Benjamin; Narula, Jagat; Hofer, Ira; Singh, Karandeep; Nadkarni, Girish N.
  • Vaid A; Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.).
  • Sawant A; Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.S.).
  • Suarez-Farinas M; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.).
  • Lee J; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.).
  • Kaul S; Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (S.K.).
  • Kovatch P; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.).
  • Freeman R; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (R.F.).
  • Jiang J; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (J.J.).
  • Jayaraman P; Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.).
  • Fayad Z; BioMedical Engineering and Imaging Institute and Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (Z.F.).
  • Argulian E; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.).
  • Lerakis S; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.).
  • Charney AW; The Charles Bronfman Institute of Personalized Medicine and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, and Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (A.W.C.).
  • Wang F; Department of Population Health Sciences, Weill Cornell Medicine, New York, New York (F.W.).
  • Levin M; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (M.L.).
  • Glicksberg B; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.).
  • Narula J; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.).
  • Hofer I; Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York (I.H.).
  • Singh K; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan (K.S.).
  • Nadkarni GN; Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (G.N.N.).
Ann Intern Med ; 176(10): 1358-1369, 2023 10.
Article en En | MEDLINE | ID: mdl-37812781
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Lesión Renal Aguda Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Lesión Renal Aguda Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article