The increasing availability of large collections of electronic health record (EHR) data and
unprecedented technical advances in deep learning (DL) have sparked a surge of research …

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the
problem of explainability is as old as AI itself and classic AI represented comprehensible …

XAI refers to the techniques and methods for building AI applications which assist end users
to interpret output and predictions of AI models. Black box AI applications in high-stakes …

Deep learning methods have been very effective for a variety of medical diagnostic tasks
and have even outperformed human experts on some of those. However, the black-box …

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 …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

Deep neural network models are emerging as an important method in healthcare delivery,
following the recent success in other domains such as image recognition. Due to the …

The usage of black-box classification models within the healthcare field is highly dependent
on being interpretable by the receiver. Local Interpretable Model-Agnostic Explanation …

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 …

Building a predictive model based on historical Electronic Health Records (EHRs) for
personalized healthcare has become an active research area. Benefiting from the powerful …