Results 281 to 290 of about 492,317 (384)
Using Convolutional Neural Networks for the Classification of Suboptimal Chest Radiographs
This study evaluated DenseNet121 and YOLOv8 neural networks in detecting suboptimal chest x‐rays for quality control. Through training, validation, and testing, both AI models effectively classified chest X‐ray quality, highlighting the potential to provide radiographers with feedback to enhance image quality.
Emily Huanke Liu +2 more
wiley +1 more source
Development trajectory of humanistic care and occupational calling in nursing students during internship: a multi-center longitudinal study. [PDF]
Su M, Deng Q, Hu Y, Chen Y.
europepmc +1 more source
This study developed standardised Medical Imaging Suite (MIS) request and contrast consent forms, modelled on real‐world documentation, to enhance student learning in diagnostic radiography education. By analysing forms from n = 25 medical imaging providers, key fields were identified and incorporated into authentic teaching resources, intended for use
Don J. Nocum +2 more
wiley +1 more source
Nursing Educators' Experiences of Clinical Internships during Coronavirus Pandemic (COVID-19): A Qualitative Study. [PDF]
Mardanian Dehkordi L +4 more
europepmc +1 more source
Academic Health Collaborative of Worcester Summer Internships Program Clark University 2016 Report
Hannah Silverfine
openalex +1 more source
Artificial Intelligence for Radiographic Image Quality: Radiographers at the Forefront
This editorial highlights the central role of radiographers in leading AI‐driven radiographic image‐quality assessment. It outlines how AI can enhance real‐time feedback, support consistency, and strengthen safe, patient‐centered imaging practice.
Kamarul Amin Abdullah
wiley +1 more source
Exploring self-perceived preparedness for practice among pre and post Covid-19 graduates of Nigerian dental schools. [PDF]
Oginni FO +3 more
europepmc +1 more source

