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Teaching in the Digital Age-Developing a Support Program for Nursing Education Providers: Design-Based Research.
JMIR Form Res
January 2025
Institute of Nursing Science, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background: Health care systems and the nursing profession worldwide are being transformed by technology and digitalization. Nurses acquire digital competence through their own experience in daily practice, but also from education and training; nursing education providers thus play an important role. While nursing education providers have some level of digital competence, there is a need for ongoing training and support for them to develop more advanced skills and effectively integrate technology into their teaching.
View Article and Find Full Text PDFMerging motoneuron and postural synergies in prosthetic hand design for natural bionic interfacing.
Sci Robot
January 2025
Department of Bioengineering, Imperial College of London, London, UK.
Despite the advances in bionic reconstruction of missing limbs, the control of robotic limbs is still limited and, in most cases, not felt to be as natural by users. In this study, we introduce a control approach that combines robotic design based on postural synergies and neural decoding of synergistic behavior of spinal motoneurons. We developed a soft prosthetic hand with two degrees of actuation that realizes postures in a two-dimensional linear manifold generated by two postural synergies.
View Article and Find Full Text PDFHarnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review.
JMIR Ment Health
December 2024
Innovation Horizons, Inc., 2819 27th Street, NW, Washington, DC, US.
Background: Access to accurate medical diagnosis has been hindered by socioeconomic disparities, limited availability of specialized medical professionals, and lack of patient education, among other factors. Inequities in access to high-quality healthcare services exacerbate these challenges, often leading to disparities in health outcomes. Missed or inaccurate diagnoses can lead to delayed or unnecessary treatments, risking worsening of the condition.
View Article and Find Full Text PDFObjective surgical skill assessment based on automatic recognition of dissection and exposure times in robot-assisted radical prostatectomy.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFEvaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.
Insights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
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