Artificial Intelligence-Generated Editorials in Radiology: Can Expert Editors Detect Them?

Journal: AJNR. American journal of neuroradiology
PMID: 39288967

Abstract

BACKGROUND AND PURPOSE: Artificial intelligence is capable of generating complex texts that may be indistinguishable from those written by humans. We aimed to evaluate the ability of GPT-4 to write radiology editorials and to compare these with human-written counterparts, thereby determining their real-world applicability for scientific writing.

Authors

  • Burak Berksu Ozkara
    Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA.
  • Alexandre Boutet
    Joint Department of Medical Imaging, University of Toronto, Toronto, Canada.
  • Bryan A Comstock
    Department of Biostatistics, University of Washington, Seattle, WA, USA.
  • Johan Van Goethem
    Department of Radiology (J.V.G.), Antwerp University Hospital, Antwerp, Belgium.
  • Thierry A G M Huisman
    Edward B. Singleton Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA.
  • Jeffrey S Ross
    Department of Radiology (J.S.R.), Mayo Clinic Arizona, Phoenix, Arizona.
  • Luca Saba
    Department of Radiology, A.O.U., Italy.
  • Lubdha M Shah
    Department of Radiology (L.M.S.), University of Utah, Salt Lake City, Utah.
  • Max Wintermark
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Mauricio Castillo

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