The use of artificial intelligence (AI) has grown dramatically in the past few years in the
United States and worldwide, with more than 300 AI-enabled devices approved by the US …

In this review, concepts of algorithmic bias and fairness are defined qualitatively and
mathematically. Illustrative examples are given of what can go wrong when unintended bias …

Background The specialization and complexity of radiology makes the automatic generation
of radiologic impressions (ie, a diagnosis with differential diagnosis and management …

A human decision-maker benefits the most from an AI assistant that corrects for their biases.
For problems such as generating interpretation of a radiology report given findings, a system …

Cognitive biases are systematic thought processes involving the use of a filter of personal
experiences and preferences arising from the tendency of the human brain to simplify …

Rationale and objective: Image interpretation is a fundamental aspect of radiology. The
treatment and management of patients relies on accurate and timely imaging diagnosis …

A large‐scale postmortem auditing of antemortem imaging diagnoses has yet to be
accomplished in veterinary medicine. For this retrospective, observational, single‐center …

Abstract While Artificial Intelligence (AI) has the potential to transform the field of diagnostic
radiology, important obstacles still inhibit its integration into clinical environments. Foremost …

Objective: In this proof-of-concept study, we aimed to develop deep-learning-based
classifiers to identify rib fractures on frontal chest radiographs in children under 2 years of …

Radiological interpretations, while essential, are not infallible and are best understood as
expert opinions formed through the evaluation of available evidence. Acknowledging the …