Abstract Background AI/ML CAD tools can potentially improve outcomes in the high-stakes,
high-volume model of trauma radiology. No prior scoping review has been undertaken to …

Purpose There is a growing body of diagnostic performance studies for emergency
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …

AIM To evaluate the performance of a machine learning based algorithm tool for chest
radiographs (CXRs), applied to a consecutive cohort of historical clinical cases, in …

Background Reproducible approaches are needed to bring AI/ML for medical image
analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical …

Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent
years. Although many of the first clinical applications were in the neuro, cardiothoracic, and …

Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in
various settings. However, like any technology, it must be used with caution and awareness …

Motivation Our ability to identify causative genetic factors for mouse genetic models of
human diseases and biomedical traits has been limited by the difficulties associated with …

Purpose To evaluate the performance of a deep learning-based computer-aided detection
(CAD) software for detecting pulmonary nodules, masses, and consolidation on chest …

Background precision-medicine quantitative tools for cross-sectional imaging require
painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data …

Alex M. Aisen, MD, is a retired academic radiologist who specialized in gastrointestinal and
body imaging. He began his career at the University of Michigan and moved midcareer to …