From technical to understandable: Artificial Intelligence Large Language Models improve the readability of knee radiology reports.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Published Date: Mar 15, 2024

Abstract

PURPOSE: The purpose of this study was to evaluate the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of knee 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.
  • James Puleo
    Albany Medical Center, Albany, New York, USA.
  • Michael C Harrington
    Department of Orthopedic Surgery, Albany Medical Center, Albany, New York, USA.
  • Jari Dahmen
    Department of Orthopaedic Surgery and Sports Medicine, Amsterdam Movement Sciences, Amsterdam UMC, Location AMC, University of Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands. j.dahmen@amsterdamumc.nl.
  • Andrew J Rosenbaum
    Department of Orthopedic Surgery, Albany Medical Center, Albany, New York, USA.
  • Gino M M J Kerkhoffs
  • 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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