Researchers at the University of Copenhagen have demonstrated that using a commercially available AI system can reduce the workload of radiologists by over 33% while enhancing the overall performance of breast cancer screenings.

Mammography exams are routinely used for early detection of breast cancer, significantly reducing mortality rates. However, these exams create a substantial workload for radiologists, who must read numerous mammograms, most of which do not warrant patient recall. “The reading workload is further compounded when screening programs employ double reading to improve cancer detection and decrease false-positive recalls,” said Andreas D. Lauritzen, PhD, a researcher at the University of Copenhagen and Gentofte Hospital in Denmark. Recalling women for further tests also increases the workload, a challenge exacerbated by the shortage of specialized breast radiologists.

An innovative approach to artificial intelligence (AI) now enables the reconstruction of extensive datasets, such as overall ocean temperature, from a small number of field-deployable sensors using low-powered edge computing. This method holds broad applications across industry, science, and medicine.

“We developed a neural network that allows us to represent a large system in a very compact way,” said Javier Santos, a researcher at Los Alamos National Laboratory. “That compactness means it requires fewer computing resources compared to state-of-the-art convolutional neural network architectures, making it well-suited to field deployment on drones, sensor arrays, and other edge-computing applications that put computation closer to its end use.”