Solving Complex Pediatric Surgical Case Studies: A Comparative Analysis of Copilot, ChatGPT-4, and Experienced Pediatric Surgeons' Performance.

Journal: European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
Published Date: Apr 2, 2025

Abstract

The emergence of large language models (LLMs) has led to notable advancements across multiple sectors, including medicine. Yet, their effect in pediatric surgery remains largely unexplored. This study aims to assess the ability of the artificial intelligence (AI) models ChatGPT-4 and Microsoft Copilot to propose diagnostic procedures, primary and differential diagnoses, as well as answer clinical questions using complex clinical case vignettes of classic pediatric surgical diseases.We conducted the study in April 2024. We evaluated the performance of LLMs using 13 complex clinical case vignettes of pediatric surgical diseases and compared responses to a human cohort of experienced pediatric surgeons. Additionally, pediatric surgeons rated the diagnostic recommendations of LLMs for completeness and accuracy. To determine differences in performance, we performed statistical analyses.ChatGPT-4 achieved a higher test score (52.1%) compared to Copilot (47.9%) but less than pediatric surgeons (68.8%). Overall differences in performance between ChatGPT-4, Copilot, and pediatric surgeons were found to be statistically significant ( < 0.01). ChatGPT-4 demonstrated superior performance in generating differential diagnoses compared to Copilot ( < 0.05). No statistically significant differences were found between the AI models regarding suggestions for diagnostics and primary diagnosis. Overall, the recommendations of LLMs were rated as average by pediatric surgeons.This study reveals significant limitations in the performance of AI models in pediatric surgery. Although LLMs exhibit potential across various areas, their reliability and accuracy in handling clinical decision-making tasks is limited. Further research is needed to improve AI capabilities and establish its usefulness in the clinical setting.

Authors

  • Richard Gnatzy
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Martin Lacher
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Michael Berger
    Department of Pediatric Surgery, University Hospital Essen, Essen, Germany.
  • Michael Boettcher
    Department of Pediatric Surgery, University Medical Centre Mannheim, Mannheim, Germany.
  • Oliver J Deffaa
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Joachim Kübler
    Department of Pediatric Surgery, Hospital Bremen-Mitte, Bremen, Germany.
  • Omid Madadi-Sanjani
    Department of Pediatric Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Illya Martynov
    Centre for Pediatric Surgery, Department of Pediatric Surgery and Urology, University Hospital Giessen-Marburg, Baldingerstraße, Marburg, Germany.
  • Steffi Mayer
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Mikko P Pakarinen
    Department of Pediatric Surgery, University of Helsinki Children's Hospital Unit of Pediatric Surgery, Helsinki, Finland.
  • Richard Wagner
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.
  • Tomas Wester
    Department of Pediatric Surgery, Karolinska University Hospital, Stockholm, Sweden.
  • Augusto Zani
    Department of Surgery, Division of Pediatric Surgery, Washington University School of Medicine, St. Louis, Missouri, United States.
  • Ophelia Aubert
    Department of Pediatric Surgery, Leipzig University, Leipzig, Germany.

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