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Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.
Brady, Adrian P; Allen, Bibb; Chong, Jaron; Kotter, Elmar; Kottler, Nina; Mongan, John; Oakden-Rayner, Lauren; Pinto Dos Santos, Daniel; Tang, An; Wald, Christoph; Slavotinek, John.
Afiliación
  • Brady AP; University College Cork, Cork, Ireland.
  • Allen B; Department of Radiology, Grandview Medical Center, Birmingham, Alabama, USA.
  • Chong J; American College of Radiology Data Science Institute, Reston, Virginia, USA.
  • Kotter E; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
  • Kottler N; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Mongan J; Radiology Partners, El Segundo, California, USA.
  • Oakden-Rayner L; Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, California, USA.
  • Pinto Dos Santos D; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
  • Tang A; Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia.
  • Wald C; Department of Radiology, University Hospital of Cologne, Cologne, Germany.
  • Slavotinek J; Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany.
J Med Imaging Radiat Oncol ; 68(1): 7-26, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38259140
ABSTRACT
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Inteligencia Artificial Límite: Humans País/Región como asunto: America do norte / Europa Idioma: En Revista: J Med Imaging Radiat Oncol Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS / RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Irlanda

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Radiología / Inteligencia Artificial Límite: Humans País/Región como asunto: America do norte / Europa Idioma: En Revista: J Med Imaging Radiat Oncol Asunto de la revista: DIAGNOSTICO POR IMAGEM / NEOPLASIAS / RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Irlanda