ChatGPT and Large Language Models in Radiology: Perspectives From the Field.

Journal: AJR. American journal of roentgenology
Published Date: Sep 18, 2024

Abstract

Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being recognized as tools with the potential to transform many industries, including health care. Implementation and use of these tools among radiologists is likely variable, driven by radiology practice and institutional factors. Radiologists from various practices were asked about their perspectives on generative AI and LLMs in radiology.

Authors

  • Manisha Bahl
    Massachusetts General Hospital, Department of Radiology, Boston, MA. Electronic address: mbahl1@mgh.harvard.edu.
  • Patricia Balthazar
    Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
  • Melissa A Davis
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar St. Tompkins East TE-2, New Haven, CT 06520.
  • Mina S Makary
    Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, United States of America.
  • Sree Harsha Tirumani
    Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
  • Christopher T Whitlow
    Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Division of Neuroradiology, Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Department of Biomedical Engineering, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Clinical and Translational Sciences Institute (CTSI), Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address: cwhitlow@wakehealth.edu.

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