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Aim: This study evaluated the use of a generative pre-trained transformer (GPT)-based virtual patient in nursing education.
Background: In nursing education, conventional training methods such as interactions with real-life or standardized patients exhibit limitations such as psychological distress, repetitive training and insufficient cost- and time-effectiveness. Because of their capacity to emulate human-like dialogue, GPTs have emerged as a valuable resource for educational nursing activities.
Design: This study employed a mixed-methods design.
Methods: A GPT-based virtual patient with acute appendicitis was included. Twenty-eight new prospective nurses in South Korea, equipped with a head-mounted display, evaluated and communicated with the virtual patient. Usability, perceived virtual learning environment and self-efficacy in communication were measured. The GPT-generated dialogues and open-ended questions were subjected to qualitative analysis.
Results: Among the subfactors of usability, the subdomains of perceived accessibility of functions and perceived virtual learning environments achieved high scores. Furthermore, a notable increase in self-efficacy for communication was observed (t = -2.82, p = .009). The participants' experiences with the GPT-based virtual patient were divided into "educational effects and learner experience" and "technical limitations and the need for improvement." Evaluation of the dialogue between the GPT-based virtual patient and participants revealed that the readability subdomain achieved the highest score, whereas the accuracy subdomain achieved the lowest score.
Conclusions: The findings of the present study provide insights into the advantages of employing GPT-based virtual patients, particularly regarding the perceived accessibility of functions, high scores for immersion and enhanced self-efficacy of communication.
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http://dx.doi.org/10.1016/j.nepr.2025.104536 | DOI Listing |
J Appl Clin Med Phys
September 2025
Clinical Imaging Physics Group, Duke University Health System, Durham, North Carolina, USA.
Introduction: Medical physicists play a critical role in ensuring image quality and patient safety, but their routine evaluations are limited in scope and frequency compared to the breadth of clinical imaging practices. An electronic radiologist feedback system can augment medical physics oversight for quality improvement. This work presents a novel quality feedback system integrated into the Epic electronic medical record (EMR) at a university hospital system, designed to facilitate feedback from radiologists to medical physicists and technologist leaders.
View Article and Find Full Text PDFEye (Lond)
September 2025
Genetics Laboratory, Metropolitan South Clinical Laboratory, Bellvitge University Hospital, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.
Background: Inherited retinal dystrophies (IRDs) are a genetically heterogeneous group of conditions, with approximately 40% of cases remaining unresolved after initial genetic testing. This study aimed to assess the impact of a personalised genomic approach integrating whole-exome sequencing (WES) reanalysis, whole-genome sequencing (WGS), customised gene panels and functional assays to improve diagnostic yield in unresolved cases.
Subjects/methods: We retrospectively reviewed a cohort of 597 individuals with IRDs, including 525 probands and 72 affected relatives.
NPJ Biofilms Microbiomes
September 2025
Research Group Medical Systems Biology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel University, Kiel, Schleswig-Holstein, Germany.
Urinary tract infections (UTIs) are among the most common bacterial infections and are increasingly complicated by multidrug resistance (MDR). While Escherichia coli is frequently implicated, the contribution of broader microbial communities remains less understood. Here, we integrate metatranscriptomic sequencing with genome-scale metabolic modeling to characterize active metabolic functions of patient-specific urinary microbiomes during acute UTI.
View Article and Find Full Text PDFJpn J Radiol
September 2025
Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221002, Jiangsu, China.
Background: Stroke, frequently associated with carotid artery disease, is evaluated using carotid computed tomography angiography (CTA). Dual-energy CTA (DE-CTA) enhances imaging quality but presents challenges in maintaining high image clarity with low-dose scans.
Objectives: To compare the image quality of 50 keV virtual monoenergetic images (VMI) generated using Deep Learning Image Reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-V (ASIR-V) algorithms under a triple-low scanning protocol in carotid CTA.
Background: To help reduce mental health disparities in the transgender and gender diverse (TGD) population, there is a need to equip future psychiatric mental health nurse practitioners (PMHNPs) with affirming care competence.
Method: This study evaluated a multimodal education program that combined eLearning with two virtual standardized patient (SP) simulations to teach PMHNP students to provide affirming mental health care to TGD people.
Results: Slight increases in knowledge and attitudes were not practically applicable.