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Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.
Röösli, Eliane; Rice, Brian; Hernandez-Boussard, Tina.
  • Röösli E; School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
  • Rice B; Department of Medicine (Biomedical Informatics), Stanford University, Stanford, California, USA.
  • Hernandez-Boussard T; Department of Emergency Medicine, Stanford University, Stanford, California, USA.
J Am Med Inform Assoc ; 28(1): 190-192, 2021 01 15.
Article in English | MEDLINE | ID: covidwho-1066360
ABSTRACT
The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe artificial intelligence (AI) is a solution to guide clinical decision-making for this novel disease, resulting in the rapid dissemination of underdeveloped and potentially biased models, which may exacerbate the disparities gap. We believe there is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared COVID-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Resource Allocation / Healthcare Disparities / COVID-19 Type of study: Prognostic study / Randomized controlled trials / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Resource Allocation / Healthcare Disparities / COVID-19 Type of study: Prognostic study / Randomized controlled trials / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia