This comprehensive review unfolds a detailed narrative of Artificial Intelligence (AI) making
its foray into radiology, a move that is catalysing transformational shifts in the healthcare …

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …

Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning
reconstruction (DLR) has recently emerged as a technology used in the image …

The exploration of multifunctional materials and intelligent technologies used for
fluorescence sensing and latent fingerprint (LFP) identification is a research hotspot of …

Simple Summary Radiomics could increase the value of medical images for oncologic
patients, allowing for the identification of novel imaging biomarkers and building prediction …

Objective To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and
its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging …

Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI
algorithms currently provide include detection, segmentation, classification, and …

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis,
demonstrating high performance on tasks such as cancer detection. This literature review …

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the
subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic …

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data
contained in medical images. Deep learning and radiomics are the two main AI methods …