As the role of artificial intelligence (AI) in clinical practice evolves, governance structures
oversee the implementation, maintenance, and monitoring of clinical AI algorithms to …

Purpose Machine learning (ML) and deep learning (DL) can be utilized in radiology to help
diagnosis and for predicting management and outcomes based on certain image findings …

Purpose This paper focuses on how the implementation of artificial intelligence algorithms
(AI) challenges and changes the existing communication practice in radiology seen from a …

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier
application and the future development trend of precision neuroscience. This review aimed …

Purpose We aimed to systematically analyse how the radiology community discusses the
concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations …

Background: Elucidation of the mechanism of ubiquitation has led to novel ways to treat
glioblastoma (GBM). A tripartite motif (TRIM) protein mediates a reversible, stringent …

Autologous breast reconstruction surgery is a vital part of the recovery process for patients
with breast cancer. While various reconstructive options exist, the deep inferior epigastric …

La radiologie est l'un des premiers secteurs médicaux à être concerné concrètement par
l'arrivée de dispositifs labellisés «intelligence artificielle» pour le traitement des images …

Objectives Virtual monochromatic images (VMI) are increasingly used in clinical practice as
they improve contrast-to-noise ratio. However, due to their different appearances, the …

There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with
the potential to assist veterinarians in clinical decisions. A commercially available, AI-based …