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An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 2: Implementation Considerations and Harms.
Warren, Blair Edward; Bilbily, Alexander; Gichoya, Judy Wawira; Chartier, Lucas B; Fawzy, Aly; Barragán, Camilo; Jaberi, Arash; Mafeld, Sebastian.
Afiliación
  • Warren BE; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Bilbily A; Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada.
  • Gichoya JW; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Chartier LB; 16 Bit Inc., Toronto, ON, Canada.
  • Fawzy A; Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
  • Barragán C; Department of Radiology, Emory University, Atlanta, GA, USA.
  • Jaberi A; Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada.
  • Mafeld S; Department of Emergency Medicine, University Health Network, Toronto, ON, Canada.
Can Assoc Radiol J ; 75(3): 568-574, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38445517
ABSTRACT
The introduction of artificial intelligence (AI) in interventional radiology (IR) will bring about new challenges and opportunities for patients and clinicians. AI may comprise software as a medical device or AI-integrated hardware and will require a rigorous evaluation that should be guided based on the level of risk of the implementation. A hierarchy of risk of harm and possible harms are described herein. A checklist to guide deployment of an AI in a clinical IR environment is provided. As AI continues to evolve, regulation and evaluation of the AI medical devices will need to continue to evolve to keep pace and ensure patient safety.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Radiología Intervencionista Límite: Humans Idioma: En Revista: Can Assoc Radiol J Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Radiología Intervencionista Límite: Humans Idioma: En Revista: Can Assoc Radiol J Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Estados Unidos