Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

With the power of parallel processing, large datasets, and fast computational resources,
deep neural networks (DNNs) have outperformed highly trained and experienced human …

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

The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …

Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A sco** review was conducted following the Joanna Briggs …

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 …

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