%0 Journal Article
%A Carl, Nicolas
%A Haggenmüller, Sarah
%A Wies, Christoph
%A Nguyen, Lisa
%A Winterstein, Jana Theres
%A Hetz, Martin Joachim
%A Mangold, Maurin Helen
%A Hartung, Friedrich Otto
%A Grüne, Britta
%A Holland-Letz, Tim
%A Michel, Maurice Stephan
%A Brinker, Titus
%A Wessels, Frederik
%T Evaluating interactions of patients with large language models for medical information.
%J BJU international
%V 135
%N 6
%@ 1464-4096
%C Oxford
%I Wiley-Blackwell
%M DKFZ-2025-00392
%P 1010-1017
%D 2025
%Z #EA:C140#LA:C140# / 2025 Jun;135(6):1010-1017
%X To explore the interaction of real-world patients with a chatbot in a clinical setting, investigating key aspects of medical information provided by large language models (LLMs).The study enrolled 300 patients seeking urological counselling between February and July 2024. First, participants voluntarily conversed with a Generative Pre-trained Transformer 4 (GPT-4) powered chatbot to ask questions related to their medical situation. In the following survey, patients rated the perceived utility, completeness, and understandability of the information provided during the simulated conversation as well as user-friendliness. Finally, patients were asked which, in their experience, best answered their questions: LLMs, urologists, or search engines.A total of 292 patients completed the study. The majority of patients perceived the chatbot as providing useful, complete, and understandable information, as well as being user-friendly. However, the ability of human urologists to answer medical questions in an understandable way was rated higher than of LLMs. Interestingly, 53
%K artificial intelligence (Other)
%K clinical trial (Other)
%K implementation science (Other)
%K large language models (Other)
%K patient interaction (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39967059
%R 10.1111/bju.16676
%U https://inrepo02.dkfz.de/record/298963