Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …

Although advances in deep learning systems for image-based medical diagnosis
demonstrate their potential to augment clinical decision-making, the effectiveness of …

Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …

There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence
(XAI) approaches to boost people's confidence and trust in Artificial Intelligence methods …

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI
models and is important in building trust in model predictions. XAI explanations themselves …

Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …

This systematic literature review examines state-of-the-art Explainable Artificial Intelligence
(XAI) methods applied to medical image analysis, discussing current challenges and future …

Purpose There is a growing body of diagnostic performance studies for emergency
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …

Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …