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@ARTICLE{Aghamaliyev:298349,
      author       = {U. Aghamaliyev and J. Karimbayli and A. Zamparas and F.
                      Bösch and M. Thomas and T. Schmidt and C. Krautz and C.
                      Kahlert and S. Schölch$^*$ and M. K. Angele and H. Niess
                      and M. O. Guba and J. Werner and M. Ilmer$^*$ and B. W.
                      Renz$^*$},
      title        = {{B}ots in white coats: are large language models the future
                      of patient education? a multi-center cross-sectional
                      analysis.},
      journal      = {International journal of surgery},
      volume       = {111},
      number       = {3},
      issn         = {1743-9191},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2025-00253},
      pages        = {2376-2384},
      year         = {2025},
      note         = {2025 Mar 1;111(3):2376-2384},
      abstract     = {Every year, around 300 million surgeries are conducted
                      worldwide, with an estimated 4.2 million deaths occurring
                      within 30 days after surgery. Adequate patient education is
                      crucial, but often falls short due to the stress patients
                      experience before surgery. Large language models (LLMs) can
                      significantly enhance this process by delivering thorough
                      information and addressing patient concerns that might
                      otherwise go unnoticed.This cross-sectional study evaluated
                      ChatGPT-4o's audio-based responses to frequently asked
                      questions (FAQs) regarding six general surgical procedures.
                      Three experienced surgeons and two senior residents
                      formulated seven general and three procedure-specific FAQs
                      for both preoperative and postoperative situations, covering
                      six surgical scenarios (major: pancreatic head resection,
                      rectal resection, total gastrectomy; minor: cholecystectomy,
                      Lichtenstein procedure, hemithyroidectomy). In total, 120
                      audio responses were generated, transcribed, and assessed by
                      11 surgeons from six different German university
                      hospitals.ChatGPT-4o demonstrated strong performance,
                      achieving an average score of 4.12/5 for accuracy, 4.46/5
                      for relevance, and 0.22/5 for potential harm across 120
                      questions. Postoperative responses surpassed preoperative
                      ones in both accuracy and relevance, while also exhibiting
                      lower potential for harm. Additionally, responses related to
                      minor surgeries were minimal, but significantly more
                      accurate compared to those for major surgeries.This study
                      underscores GPT-4o's potential to enhance patient education
                      both before and after surgery by delivering accurate and
                      relevant responses to FAQs about various surgical
                      procedures. Responses regarding the postoperative course
                      proved to be more accurate and less harmful than those
                      addressing preoperative ones. Although a few responses
                      carried moderate risks, the overall performance was robust,
                      indicating GPT-4o's value in patient education. The study
                      suggests the development of hospital-specific applications
                      or the integration of GPT-4o into interactive robotic
                      systems to provide patients with reliable, immediate
                      answers, thereby improving patient satisfaction and informed
                      decision-making.},
      cin          = {A430 / MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)A430-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39878073},
      doi          = {10.1097/JS9.0000000000002250},
      url          = {https://inrepo02.dkfz.de/record/298349},
}