[HTML][HTML] Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A sco** review

D Hua, N Petrina, N Young, JG Cho, SK Poon - Artificial Intelligence in …, 2024 - Elsevier
Background Artificial intelligence (AI) technology has the potential to transform medical
practice within the medical imaging industry and materially improve productivity and patient …

Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging

A Pahud de Mortanges, H Luo, SZ Shu, A Kamath… - NPJ digital …, 2024 - nature.com
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over
the last few years. While the technical developments are manifold, less focus has been …

[HTML][HTML] Applications of deep learning in trauma radiology: a narrative review

CT Cheng, CH Ooyang, CH Liao, SC Kang - Biomedical Journal, 2025 - Elsevier
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying
injuries requiring intervention. Deep learning (DL) has become mainstream in medical …

How to prepare for a bright future of radiology in Europe

M Becker - Insights into Imaging, 2023 - Springer
Because artificial intelligence (AI)-powered algorithms allow automated image analysis in a
growing number of diagnostic scenarios, some healthcare stakeholders have raised doubts …

2023 survey on user experience of artificial intelligence software in radiology by the Korean Society of Radiology

EJ Hwang, JE Park, KD Song, DH Yang… - Korean Journal of …, 2024 - pmc.ncbi.nlm.nih.gov
Objective In Korea, radiology has been positioned towards the early adoption of artificial
intelligence-based software as medical devices (AI-SaMDs); however, little is known about …

Pulmonary contusion: automated deep learning-based quantitative visualization

N Sarkar, L Zhang, P Campbell, Y Liang, G Li… - Emergency …, 2023 - Springer
Purpose Rapid automated CT volumetry of pulmonary contusion may predict progression to
Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in …

Deep Learning for automated detection and localization of traumatic abdominal solid organ injuries on CT scans

CT Cheng, HH Lin, CP Hsu, HW Chen… - Journal of Imaging …, 2024 - Springer
Computed tomography (CT) is the most commonly used diagnostic modality for blunt
abdominal trauma (BAT), significantly influencing management approaches. Deep learning …

A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow

L Zhang, W LaBelle, M Unberath, H Chen, J Hu… - Frontiers in …, 2023 - frontiersin.org
Background Reproducible approaches are needed to bring AI/ML for medical image
analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical …

'Humans think outside the pixels'–Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting

JV Johansson, E Engström - Health Informatics Journal, 2024 - journals.sagepub.com
Objective This study aimed to explore radiologists' views on using an artificial intelligence
(AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support …

RSNA 2023 Abdominal Trauma AI Challenge Review and Outcomes Analysis

S Hermans, Z Hu, RL Ball, HM Lin… - Radiology: Artificial …, 2024 - pubs.rsna.org
“Just Accepted” papers have undergone full peer review and have been accepted for
publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout …