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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; Dos Santos, Daniel Pinto; Tang, An; Wald, Christoph; Slavotinek, John.
Afiliación
  • Brady AP; University College Cork, Cork, Ireland. adrianbrady@me.com.
  • Allen B; Department of Radiology, Grandview Medical Center, Birmingham, AL, USA.
  • Chong J; American College of Radiology Data Science Institute, Reston, VA, USA.
  • Kotter E; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, 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, CA, USA.
  • Oakden-Rayner L; Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, CA, USA.
  • Dos Santos DP; Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA.
  • Tang A; Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.
  • Wald C; Department of Radiology, University Hospital of Cologne, Cologne, Germany.
  • Slavotinek J; Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany.
Insights Imaging ; 15(1): 16, 2024 Jan 22.
Article en En | MEDLINE | ID: mdl-38246898
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.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article País de afiliación: Irlanda

Texto completo: 1 Banco de datos: MEDLINE Idioma: En Revista: Insights Imaging Año: 2024 Tipo del documento: Article País de afiliación: Irlanda