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Status: Bibliographieeintrag

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Verfasst von:Baumgärtner, Kilian [VerfasserIn]   i
 Byczkowski, Michael [VerfasserIn]   i
 Schmid, Tamara [VerfasserIn]   i
 Muschko, Marc [VerfasserIn]   i
 Woessner, Philipp [VerfasserIn]   i
 Gerlach, Axel [VerfasserIn]   i
 Bonekamp, David [VerfasserIn]   i
 Schlemmer, Heinz-Peter [VerfasserIn]   i
 Hohenfellner, Markus [VerfasserIn]   i
 Görtz, Magdalena [VerfasserIn]   i
Titel:Effectiveness of the medical chatbot PROSCA to inform patients about prostate cancer
Titelzusatz:results of a randomized controlled trial
Verf.angabe:Kilian Baumgärtner, Michael Byczkowski, Tamara Schmid, Marc Muschko, Philipp Woessner, Axel Gerlach, David Bonekamp, Heinz-Peter Schlemmer, Markus Hohenfellner, Magdalena Görtz
E-Jahr:2024
Jahr:[17 September 2024]
Umfang:9 S.
Fussnoten:Gesehen am 27.03.2025
Titel Quelle:Enthalten in: European urology open science
Ort Quelle:[Amsterdam] : Elsevier ScienceDirect, 2020
Jahr Quelle:2024
Band/Heft Quelle:69(2024), Seite 80-88
ISSN Quelle:2666-1683
Abstract:Background and objective - Artificial intelligence (AI)-powered conversational agents are increasingly finding application in health care, as these can provide patient education at any time. However, their effectiveness in medical settings remains largely unexplored. This study aimed to assess the impact of the chatbot “PROState cancer Conversational Agent” (PROSCA), which was trained to provide validated support from diagnostic tests to treatment options for men facing prostate cancer (PC) diagnosis. - Methods - The chatbot PROSCA, developed by urologists at Heidelberg University Hospital and SAP SE, was evaluated through a randomized controlled trial (RCT). Patients were assigned to either the chatbot group, receiving additional access to PROSCA alongside standard information by urologists, or the control group (1:1), receiving standard information. A total of 112 men were included, of whom 103 gave feedback at study completion. - Key findings and limitations - Over time, patients’ information needs decreased significantly more in the chatbot group than in the control group (p = 0.035). In the chatbot group, 43/54 men (79.6%) used PROSCA, and all of them found it easy to use. Of the men, 71.4% agreed that the chatbot improved their informedness about PC and 90.7% would like to use PROSCA again. Limitations are study sample size, single-center design, and specific clinical application. - Conclusions and clinical implications - With the introduction of the PROSCA chatbot, we created and evaluated an innovative, evidence-based AI health information tool as an additional source of information for PC. Our RCT results showed significant benefits of the chatbot in reducing patients’ information needs and enhancing their understanding of PC. This easy-to-use AI tool provides accurate, timely, and accessible support, demonstrating its value in the PC diagnosis process. Future steps include further customization of the chatbot’s responses and integration with the existing health care systems to maximize its impact on patient outcomes. - Patient summary - This study evaluated an artificial intelligence-powered chatbot—PROSCA, a digital tool designed to support men facing prostate cancer diagnosis by providing validated information from diagnosis to treatment. Results showed that patients who used the chatbot as an additional tool felt better informed than those who received standard information from urologists. The majority of users appreciated the ease of use of the chatbot and expressed a desire to use it again; this suggests that PROSCA could be a valuable resource to improve patient understanding in prostate cancer diagnosis.
DOI:doi:10.1016/j.euros.2024.08.022
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1016/j.euros.2024.08.022
 kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S2666168324006554
 DOI: https://doi.org/10.1016/j.euros.2024.08.022
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Artificial intelligence
 Chatbot
 Early detection
 Large language model
 Natural language processing
 Prostate cancer
 Randomized controlled trial
K10plus-PPN:1920731091
Verknüpfungen:→ Zeitschrift

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