Despite frequent reports of imaging artificial intelligence (AI) that parallels human
performance, clinicians often question the safety and robustness of AI products in practice …

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has
significantly impacted radiology, introducing an array of AI solutions for interpretative tasks …

Due to variations in unique patient populations, imaging hardware, and drift, artificial
intelligence (AI) models perform differently in different clinical practices. The validation of …

Objectives This study aims to contribute to an understanding of the explainability of
computer aided diagnosis studies in radiology that use end-to-end deep learning by …

Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate
health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of …

The 2018 AUR Academic Radiology and Industry Leaders Roundtable “Artificial Intelligence
(AI) in Radiology” discussion gives a high-level perspective of how AI is shaping the field of …

In this paper, we demonstrate the use of a “Challenge Dataset”: a small, site-specific,
manually curated dataset–enriched with uncommon, risk-exposing, and clinically important …

Artificial intelligence (AI) will likely affect various fields of medicine. This article aims to
explain the fundamental principles of clinical validation, device approval, and insurance …

The rapid development of information technology and data processing capabilities has led
to the creation of new tools known as artificial intelligence (AI). Medical applications of AI are …

The workload of radiologists has seen relentless growth in recent years [1]. While the
increase of raw image data is not linearly linked to increased reading time, and there is no …