TY  - JOUR
AU  - Moser, Rebecca
AU  - Buchecker, Lena Marie
AU  - Nano, Jana
AU  - Mayr, Nina A
AU  - Behzadi, Sophie T
AU  - Kiesl, Sophia
AU  - Maier, Sophie
AU  - Allwohn, Luisa
AU  - Lammert, Jacqueline
AU  - Adams, Lisa Christine
AU  - Tschochohei, Max
AU  - Combs, Stephanie E
AU  - Borm, Kai J
TI  - Attitudes Towards Large Language Model-based AI Systems as an Information Source for Shared Decision Making in Radiation Oncology.
JO  - The oncologist
VL  - nn
SN  - 1083-7159
CY  - Oxford
PB  - Oxford University Press
M1  - DKFZ-2025-03034
SP  - nn
PY  - 2025
N1  - epub
AB  - 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
KW  - ChatGPT (Other)
KW  - Large Language Models (LLMs) (Other)
KW  - artificial intelligence (AI) (Other)
KW  - cancer care (Other)
KW  - radiotherapy (Other)
KW  - shared decision making (SDM) (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:41429565
DO  - DOI:10.1093/oncolo/oyaf414
UR  - https://inrepo02.dkfz.de/record/307435
ER  -