Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …

In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …

The purpose of this paper is to propose a novel hybrid framework for evaluating and
benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi …

Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic
imaging for various diseases and modalities and therefore has a high potential to be used …

Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …

Physiological and physical motions of the subjects, eg, patients, are the primary sources of
image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring …

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of
diagnosis and staging, refined surveillance strategies, and introduced specific and …

Anomaly detection (AD) is a challenging problem in computer vision. Particularly in the field
of medical imaging, AD poses even more challenges due to a number of reasons, including …

Objectives To define requirements that condition trust in artificial intelligence (AI) as clinical
decision support in radiology from the perspective of various stakeholders and to explore …

Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …