Saliency maps can explain how deep neural networks classify images. But are they actually
useful for humans? The present systematic review of 68 user studies found that while …

This paper conducts a systematic literature review (SLR) to evaluate the effectiveness of
computational persuasion technology (CPT) in the eHealth domain. Over the past fifteen …

The furniture manufacturing sector faces significant challenges in machining composite
materials, where quality issues such as delamination can lead to substandard products. This …

A narrative review is used to develop a theoretical evidence-based means-end framework to
build an epistemic foundation to uphold explainable artificial intelligence instruments so that …

Ovarian cancer is a dangerous gynaecological malignancy, and the presence of many
subtypes causes significant diagnostic difficulties. In general, the high accuracy of …

Nowadays, artificial intelligence in medicine plays a leading role. This necessitates the need
to ensure that artificial intelligence systems are not only high-performing but also …

Explainable Machine Learning (ML) models are an essential component of Clinical Decision
Support Systems (CDSS), since they provide the transparency and interpretability that are …

This research investigates the use of bio-inspired techniques in explainable AI (XAI) to
predict Alzheimer's disease (AD). Alzheimer's disease is a neurological disease that makes …

We consider the effectiveness of multi-objective counterfactual explanations (MOCE) in
helping individuals learn tactics, or rules of thumb, to apply when required to select a course …

Artificial intelligence in healthcare has the potential to enhance diagnostics, patient care,
and medical research. However, trust in AI-driven decision-making processes is crucial as AI …