Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated
magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image …

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods,
particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and …

A crucial challenge in critical settings like medical diagnosis is making deep learning
models used in decision-making systems interpretable. Efforts in Explainable Artificial …

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human
experts, especially from non-computational domains, approach artificial intelligence; this is …

Feature attribution methods (AMs) are a simple means to provide explanations for the
predictions of black-box models such as neural networks. Due to their conceptual …

A key issue in critical contexts such as medical diagnosis is the interpretability of the deep
learning models adopted in decision-making systems. Research in eXplainable Artificial …

Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution,
exerting a profound influence on neuroimaging data analysis. This development has …

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first
competency-based applied artificial intelligence curriculum at the higher-education …

Machine learning models are increasingly being used across diverse fields, including file
fragment classification. As these models become more prevalent, it is crucial to understand …