% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bendella:281369,
      author       = {Bendella, Zeynep and Wichtmann, Barbara Daria and Clauberg,
                      Ralf and Keil, Vera C and Lehnen, Nils C and Haase, Robert
                      and Sáez, Laura C and Wiest, Isabella C and Kather, Jakob
                      Nikolas and Endler, Christoph and Radbruch, Alexander and
                      Paech, Daniel and Deike, Katerina},
      title        = {{C}hat {GPT}-4 shows high agreement in {MRI} protocol
                      selection compared to board-certified neuroradiologists.},
      journal      = {European journal of radiology},
      volume       = {193},
      issn         = {0720-048X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2025-01116},
      pages        = {112416},
      year         = {2025},
      abstract     = {The aim of this study was to determine whether ChatGPT-4
                      can correctly suggest MRI protocols and additional MRI
                      sequences based on real-world Radiology Request Forms (RRFs)
                      as well as to investigate the ability of ChatGPT-4 to
                      suggest time saving protocols.Retrospectively, 1,001 RRFs of
                      our Department of Neuroradiology (in-house dataset), 200
                      RRFs of an independent Department of General Radiology
                      (independent dataset) and 300 RRFs from an external, foreign
                      Department of Neuroradiology (external dataset) were
                      included. Patients' age, sex, and clinical information were
                      extracted from the RRFs and used to prompt ChatGPT- 4 to
                      choose an adequate MRI protocol from predefined
                      institutional lists. Four independent raters then assessed
                      its performance. Additionally, ChatGPT-4 was tasked with
                      creating case-specific protocols aimed at saving time.Two
                      and 7 of 1,001 protocol suggestions of ChatGPT-4 were rated
                      'unacceptable' in the in-house dataset for reader 1 and 2,
                      respectively. No protocol suggestions were rated
                      'unacceptable' in both the independent and external dataset.
                      When assessing the inter-reader agreement, Coheńs weighted
                      ĸ ranged from 0.88 to 0.98 (each p < 0.001). ChatGPT-4's
                      freely composed protocols were approved in 766/1,001 (76.5
                      $\%)$ and 140/300 (46.67 $\%)$ cases of the in-house and
                      external dataset with mean time savings (standard deviation)
                      of 3:51 (minutes:seconds) (±2:40) minutes and 2:59 (±3:42)
                      minutes per adopted in-house and external MRI
                      protocol.ChatGPT-4 demonstrated a very high agreement with
                      board-certified (neuro-)radiologists in selecting MRI
                      protocols and was able to suggest approved time saving
                      protocols from the set of available sequences.},
      keywords     = {ChatGPT-4 (Other) / Large language model (LLM) (Other) /
                      MRI protocol (Other) / Radiology request form (Other)},
      cin          = {AG Radbruch},
      ddc          = {610},
      cid          = {I:(DE-2719)5000075},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40961911},
      doi          = {10.1016/j.ejrad.2025.112416},
      url          = {https://pub.dzne.de/record/281369},
}