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Environmental Sustainability and AI in Radiology: A Double-Edged Sword.
Doo, Florence X; Vosshenrich, Jan; Cook, Tessa S; Moy, Linda; Almeida, Eduardo P R P; Woolen, Sean A; Gichoya, Judy Wawira; Heye, Tobias; Hanneman, Kate.
Afiliação
  • Doo FX; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Vosshenrich J; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Cook TS; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Moy L; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Almeida EPRP; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Woolen SA; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Gichoya JW; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Heye T; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
  • Hanneman K; From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University,
Radiology ; 310(2): e232030, 2024 Feb.
Article em En | MEDLINE | ID: mdl-38411520
ABSTRACT
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Limite: Humans Idioma: En Revista: Radiology Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Limite: Humans Idioma: En Revista: Radiology Ano de publicação: 2024 Tipo de documento: Article