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@ARTICLE{Moser:307435,
      author       = {R. Moser and L. M. Buchecker and J. Nano and N. A. Mayr and
                      S. T. Behzadi and S. Kiesl and S. Maier and L. Allwohn and
                      J. Lammert$^*$ and L. C. Adams and M. Tschochohei and S. E.
                      Combs$^*$ and K. J. Borm},
      title        = {{A}ttitudes {T}owards {L}arge {L}anguage {M}odel-based {AI}
                      {S}ystems as an {I}nformation {S}ource for {S}hared
                      {D}ecision {M}aking in {R}adiation {O}ncology.},
      journal      = {The oncologist},
      volume       = {nn},
      issn         = {1083-7159},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-03034},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Implementing structured shared decision making (SDM)
                      requires high-quality, reliable patient information. In
                      radiation oncology, patients often have limited knowledge
                      and misconceptions about therapy and side effects, affecting
                      their decision-making. Large Language Model-based AI systems
                      (LLMs) may help by providing evidence-based information in
                      accessible language, but successful implementation depends
                      on the willingness of patients and health care professionals
                      (HCPs) to adopt these technologies.A survey was conducted
                      among patients undergoing radiation therapy and HCPs between
                      03/2024-02/2025. Data was collected using structured
                      electronic questionnaires (32 items for patients, 35 for
                      HCPs). The survey assessed sociodemographic characteristics,
                      the status of SDM in oncology, sources of information
                      relevant to SDM, and current and anticipated LLM
                      applications. Data were analyzed using descriptive
                      statistics and logistic regression analysis.The internet was
                      the prime information source for patients (n = 400).
                      Regarding current use of LLMs, a large discrepancy between
                      patients and HCPs (n = 200) was observed $(18.2\%$ vs.
                      $69.5\%).$ Although $77\%$ of HCPs believed that patients
                      will rely on LLMs in the future, only $29.1\%$ of patients
                      agreed. Most patients $(65.8\%)$ stated that even as LLMs
                      improve, they will continue to trust physicians more; $46\%$
                      of HCPs shared this view. Only $16.5\%$ of patients were
                      convinced that LLMs provide all relevant data for SDM in
                      cancer care. Familiarity with technology was the strongest
                      predictor of LLM use among patients.Only a minority of
                      radiation oncology patients currently use LLMs, and many
                      remain skeptical about their future role-contrasting with
                      the more optimistic expectations of HCPs.},
      keywords     = {ChatGPT (Other) / Large Language Models (LLMs) (Other) /
                      artificial intelligence (AI) (Other) / cancer care (Other) /
                      radiotherapy (Other) / shared decision making (SDM) (Other)},
      cin          = {MU01},
      ddc          = {610},
      cid          = {I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41429565},
      doi          = {10.1093/oncolo/oyaf414},
      url          = {https://inrepo02.dkfz.de/record/307435},
}