Introduction Recent developments in the field of Artificial Intelligence (AI) applied to
healthcare promise to solve many of the existing global issues in advancing human health …

Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI)
models that use real-world electronic health record data, categorize these techniques …

Background Artificial intelligence (AI) for healthcare presents potential solutions to some of
the challenges faced by health systems around the world. However, it is well established in …

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of
healthcare applications, both clinical and remote, but the best performing AI systems are …

To achieve high accuracy in machine learning (ML) systems, practitioners often use complex
“black-box” models that are not easily understood by humans. The opacity of such models …

Background Significant efforts have been made to develop artificial intelligence (AI)
solutions for health care improvement. Despite the enthusiasm, health care professionals …

Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …

Details of the designs and mechanisms in support of human-AI collaboration must be
considered in the real-world fielding of AI technologies. A critical aspect of interaction design …

Several principles have been proposed to improve use of artificial intelligence (AI) in
healthcare, but the need for AI to improve longstanding healthcare challenges has not been …

Natural language processing (NLP) is a set of automated methods to organise and evaluate
the information contained in unstructured clinical notes, which are a rich source of real-world …