Comparative analysis of clinical relevance and accuracy in AI-assisted patient consultations on ankle and clavicle fracture surgeries.

Journal: Injury
Published Date:

Abstract

BACKGROUND: It is becoming increasingly important to evaluate the effectiveness of large language models (LLMs) and query-assisted platforms like Google and ChatGPT in providing clinically relevant and accurate information to patient-initiated inquiries. Limited studies have characterized the performance of these platforms on semi-elective orthopedic trauma surgeries. This study evaluates the function of both interactive online models on frequently queried patient searches on ankle and clavicle fracture operations.

Authors

  • Daniel E Pereira
    Washington University School of Medicine, Department of Orthopaedics, St. Louis, MO, USA. Electronic address: d.e.pereira@wustl.edu.
  • Helena F Barber
    Washington University School of Medicine, Department of Orthopaedics, St. Louis, MO, USA.
  • Carrie N Reaver
    Washington University School of Medicine, Department of Orthopaedics, St. Louis, MO, USA.
  • Anna N Miller
    Dartmouth Hitchcock Medical Center and Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.