Introduction: Artificial intelligence (AI) is increasingly used in healthcare. AI-based chatbots can act as automated conversational agents, capable of promoting health and providing education at any time. The objective of this study was to develop and evaluate a user-friendly medical chatbot (prostate cancer communication assistant (PROSCA)) for provisioning patient information about early detection of prostate cancer (PC).
Methods: The chatbot was developed to provide information on prostate diseases, diagnostic tests for PC detection, stages, and treatment options. Ten men aged 49 to 81 years with suspicion of PC were enrolled in this study. Nine of ten patients used the chatbot during the evaluation period and filled out the questionnaires on usage and usability, perceived benefits, and potential for improvement.
Results: The chatbot was straightforward to use, with 78% of users not needing any assistance during usage. In total, 89% of the chatbot users in the study experienced a clear to moderate increase in knowledge about PC through the chatbot. All study participants who tested the chatbot would like to re-use a medical chatbot in the future and support the use of chatbots in the clinical routine.
Conclusions: Through the introduction of the chatbot PROSCA, we created and evaluated an innovative evidence-based health information tool in the field of PC, allowing targeted support for doctor-patient communication and offering great potential in raising awareness, patient education, and support. Our study revealed that a medical chatbot in the field of early PC detection is readily accepted and benefits patients as an additional informative tool.
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http://dx.doi.org/10.1177/20552076231173304 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Huashan Hospital and Human Phenome Institute, Fudan University, 220 Handan Road, Shanghai, 200433, China.
Objective: This study aims to conduct a bibliometric analysis to explore research trends, collaboration patterns, and emerging themes in the PET/MR field based on published literature from 2010 to 2024.
Methods: A detailed literature search was performed using the Web of Science Core Collection (WoSCC) database with keywords related to PET/MR. A total of 4,349 publications were retrieved and analyzed using various bibliometric tools, including VOSviewer and CiteSpace.
Aging (Albany NY)
January 2025
Department of Public Health Sciences, University of Chicago, Chicago, IL 60615, USA.
Background: DNA methylation (DNAm) data from human samples has been leveraged to develop "epigenetic clock" algorithms that predict age and other aging-related phenotypes. Some DNAm clocks were trained using DNAm obtained from blood cells, while other clocks were trained using data from diverse tissue/cell types. To assess how DNAm clocks perform across non-blood tissue types, we applied DNAm algorithms to DNAm data generated from 9 different human tissue types.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Institute for Health and Nursing Science, Faculty of Medicine, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.
Background: Cancer requires interdisciplinary intersectoral care. The Care Coordination Instrument (CCI) captures patients' perspectives on cancer care coordination. We aimed to translate, adapt, and validate the CCI for Germany (CCI German version).
View Article and Find Full Text PDFRandomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk remains unclear. In this study, we developed TrialTranslator, a framework designed to systematically evaluate the generalizability of RCTs for oncology therapies.
View Article and Find Full Text PDFSci Rep
January 2025
Department of MRI, Zhongshan City People's Hospital, No. 2, Sunwen East Road, Shiqi District, Zhongshan, 528403, Guangdong, China.
To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features.
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