The unprecedented growth of computational capabilities in recent years has allowed
Artificial Intelligence (AI) models to be developed for medical applications with remarkable …

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

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

This paper develops a rigorous argument for why the use of Shapley values in explainable
AI (XAI) will necessarily yield provably misleading information about the relative importance …

XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of
complex models. XAI is especially required in sensitive applications, eg in health care, when …

Abstract Explainable Artificial Intelligence (XAI) is widely considered to be critical for building
trust into the deployment of systems that integrate the use of machine learning (ML) models …

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

Abstract Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable
results on various metrics primarily due to recent advancements in computational …

Neurodevelopmental disorders (NDDs) constitute a major health issue with> 10% of the
general worldwide population affected by at least one of these conditions—such as autism …

Recent work demonstrated the existence of Boolean functions for which Shapley values
provide misleading information about the relative importance of features in rule-based …