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Emerging research suggests combined artificial intelligence (AI) assessment of digital mammography and automated 3D breast ultrasound provides enhanced detection of breast cancer in women with dense breasts and may be a viable alternative in areas where radiologists are scarce.

In a study involving patients who presented to emergency departments with acute chest pain, a deep learning model demonstrated significantly improved prediction of aortic dissection and all-cause mortality and indicated that additional pulmonary and cardiovascular testing could be deferred in seven times as many patients as suggested by conventional risk factors and testing measures.

The artificial intelligence (AI)-enabled Viz™ Vascular Suite reportedly allows automated detection of vascular conditions, shown on computed tomography (CT) and other imaging modalities, and facilitates timely triage among interdisciplinary teams.

From incidental findings and screening for chronic obstructive pulmonary disease (COPD) to surveillance imaging protocols and the advent of artificial intelligence (AI), the authors of a new meta-analysis examine insights and emerging trends from the last two decades of research on the use of low-dose computed tomography (CT) in lung cancer screening.

In a video interview discussing one of her recent lectures at the Radiological Society for North America (RSNA) conference, Nina Kottler, M.D., M.S., noted how the combination of artificial intelligence (AI) and radiologist experience can help mitigate bias limitations with the development of AI algorithms as well as educational biases inherent to a radiologist’s training and experience.

In a recent interview at the Radiological Society of North America (RSNA) conference, Eliot Siegel, M.D., discussed a variety of potential benefits with cloud-based image management in radiology, ranging from enhanced data security and economies of scale to improved access to a variety of artificial intelligence (AI) solutions to increase efficiency.

In a recent video interview from the Radiological Society of North America (RSNA) conference, Tessa Cook, MD, PhD discussed new research on automated de-identification in radiology reports and the potential of artificial intelligence (AI) and natural language processing (NLP) to help address time-consuming challenges in the radiology workflow.

In a recent video interview, Susan Holley, MD discussed key findings from a large retrospective longitudinal study, presented at the recent Radiological Society of North America (RSNA) conference, which found that an emerging artificial intelligence (AI) model was over 24 percent more consistent than radiologist assessment of breast density.