Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and
future potential for transforming almost all aspects of medicine. However, in many …

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 …

Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …

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 …

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 …

Abstract Explainable Artificial Intelligence (XAI) is an emerging research topic of machine
learning aimed at unboxing how AI systems' black-box choices are made. This research field …

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

Advances in AI technologies have resulted in superior levels of AI-based model
performance. However, this has also led to a greater degree of model complexity, resulting …

Background Significant advancements in the field of information technology have influenced
the creation of trustworthy explainable artificial intelligence (XAI) in the field of healthcare …

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing
the implementation challenges of AI/ML in healthcare. However, little is known about how …