Background Explainability is one of the most heavily debated topics when it comes to the
application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have …

Artificial intelligence (AI) has huge potential to improve the health and well-being of people,
but adoption in clinical practice is still limited. Lack of transparency is identified as one of the …

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer
applications has raised a parallel concern about its ability (or lack thereof) to provide …

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper
presents a review of the key arguments in favor and against explainability for AI-powered …

The use of artificial intelligence (AI) systems in biomedical and clinical settings can disrupt
the traditional doctor–patient relationship, which is based on trust and transparency in …

Explainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that
seeks to develop methods and tools for making AI systems more explainable or …

Artificial intelligence (AI) models are increasingly finding applications in the field of
medicine. Concerns have been raised about the explainability of the decisions that are …

The increasing prevalence of digitised workflows in diagnostic pathology opens the door to
life-saving applications of artificial intelligence (AI). Explainability is identified as a critical …

The recent development of Artificial intelligence and Machine learning, in general, has
exhibited impressive results in a variety of fields, especially through the introduction of deep …