The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.

Journal: Surgical endoscopy
PMID: 38630178

Abstract

BACKGROUND: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD).

Authors

  • Bright Huo
    Division of General Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
  • Elisa Calabrese
    University of California South California, East Bay, Oakland, CA, USA.
  • Patricia Sylla
    Division of Colon and Rectal Surgery, Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Sunjay Kumar
    Department of General Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
  • Romeo C Ignacio
    Division of Pediatric Surgery/Department of Surgery, San Diego School of Medicine, University of California, California, CA, USA.
  • Rodolfo Oviedo
    Nacogdoches Center for Metabolic and Weight Loss Surgery, Nacogdoches, TX, USA.
  • Imran Hassan
    University of Iowa, Iowa City, IA, USA.
  • Bethany J Slater
    Department of Surgery, University of Chicago, Chicago, IL, USA.
  • Andreas Kaiser
    Division of Colorectal Surgery, Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA.
  • Danielle S Walsh
    Department of Surgery, University of Kentucky, Lexington, KY, USA.
  • Wesley Vosburg
    Department of Surgery, Harvard Medical School, Mount Auburn Hospital, Cambridge, MA, USA. wesvosburg@gmail.com.

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