From jargon to clarity: Improving the readability of foot and ankle radiology reports with an artificial intelligence large language model.

Journal: Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons
PMID: 38336501

Abstract

BACKGROUND: The purpose of this study was to evaluate the efficacy of an Artificial Intelligence Large Language Model (AI-LLM) at improving the readability foot and ankle orthopedic radiology reports.

Authors

  • James J Butler
    Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, 171 Delancey St, 2nd floor, New York City, USA.
  • Michael C Harrington
    Department of Orthopedic Surgery, Albany Medical Center, Albany, New York, USA.
  • Yixuan Tong
    School of Computer and Communication of the Lanzhou University of Technology, Lanzhou City, Gansu Province, China.
  • Andrew J Rosenbaum
    Department of Orthopedic Surgery, Albany Medical Center, Albany, New York, USA.
  • Alan P Samsonov
    Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, 171 Delancey St, 2nd floor, New York City, USA.
  • Raymond J Walls
    Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, 171 Delancey St, 2nd floor, New York City, USA.
  • John G Kennedy
    Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, 171 Delancey St, 2nd floor, New York City, USA. Electronic address: john.kennedy@nyulangone.org.

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