Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that
accurately detect COVID-19 in chest radiographs. However, the robustness of these systems …

High quality and efficient medical service is one of the major factors defining living
standards. Developed countries strive to make their healthcare systems as efficient and cost …

Saliency methods, which produce heat maps that highlight the areas of the medical image
that influence model prediction, are often presented to clinicians as an aid in diagnostic …

The proposed EU regulation for Artificial Intelligence (AI), the AI Act, has sparked some
debate about the role of explainable AI (XAI) in high-risk AI systems. Some argue that black …

The development of medical imaging artificial intelligence (AI) systems for evaluating COVID‐
19 patients has demonstrated potential for improving clinical decision making and assessing …

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …

Deep learning has demonstrated remarkable accuracy analyzing images for cancer
detection tasks in recent years. The accuracy that has been achieved rivals radiologists and …

Interpretability in machine learning models is important in high-stakes decisions such as
whether to order a biopsy based on a mammographic exam. Mammography poses …