The evidence of the impact of traditional statistical (TS) and artificial intelligence (AI) tool
interventions in clinical practice was limited. This study aimed to investigate the clinical …

Scarce or absent radiology resources impede adoption of artificial intelligence (AI) for
medical imaging by resource-poor health institutions. They face limitations in local …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

Objective To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and
its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging …

Rationale and Objectives To evaluate the effectiveness of an artificial intelligence (AI) in
radiology literacy course on participants from nine radiology residency programs in the …

Background: The utilization of artificial intelligence and machine learning as diagnostic and
predictive tools in perioperative medicine holds great promise. Indeed, many studies have …

The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology
continues to accelerate, and numerous algorithms are becoming available for use in clinical …

Breast cancer screening has been shown to significantly reduce mortality in women. The
increased utilization of screening examinations has led to growing demands for rapid and …

Purpose Despite tremendous gains from deep learning and the promise of artificial
intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large …

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