AI-driven simplification of surgical reports in gynecologic oncology: A potential tool for patient education.

Journal: Acta obstetricia et gynecologica Scandinavica
Published Date: May 14, 2025

Abstract

INTRODUCTION: The emergence of large language models heralds a new chapter in natural language processing, with immense potential for improving medical care and especially medical oncology. One recent and publicly available example is Generative Pretraining Transformer 4 (GPT-4). Our objective was to evaluate its ability to rephrase original surgical reports into simplified versions that are more comprehensible to patients. Specifically, we aimed to investigate and discuss the potential, limitations, and associated risks of using these simplified reports for patient education and information in gynecologic oncology.

Authors

  • Maximilian Riedel
    Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
  • Bastian Meyer
    Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
  • Raphael Kfuri Rubens
    Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
  • Caroline Riedel
    Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg, Germany.
  • Niklas Amann
    Department of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany.
  • Marion Kiechle
    Department of Gynecology and Obstetrics, Klinikum Rechts der Isar, Technical University Munich (TU), Munich, Germany.
  • Fabian Riedel
    Department of Gynecology and Obstetrics, Heidelberg University Hospital, Heidelberg, Germany.

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