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 …

Background Medical use cases for machine learning (ML) are growing exponentially. The
first hospitals are already using ML systems as decision support systems in their daily …

Artificial intelligence (AI) models for decision support have been developed for clinical
settings such as radiology, but little work evaluates the potential impact of such systems. In …

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic
decisions. Compared with rule-based systems, however, these systems are less transparent …

Background Missed fractures are a common cause of diagnostic discrepancy between initial
radiographic interpretation and the final read by board-certified radiologists. Purpose To …

Background Chest x-rays are widely used in clinical practice; however, interpretation can be
hindered by human error and a lack of experienced thoracic radiologists. Deep learning has …

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm
shift to healthcare, powered by increasing availability of healthcare data and rapid progress …

Errors and discrepancies in radiology practice are uncomfortably common, with an
estimated day-to-day rate of 3–5% of studies reported, and much higher rates reported in …

Purpose To develop and test an artificial intelligence (AI) system, based on deep
convolutional neural networks (CNNs), for automated real-time triaging of adult chest …

Rationale and Objectives To examine the effect of changes in utilization and advances in
cross-sectional imaging on radiologists' workload. Materials and Methods All computed …