Artificial Intelligence in Radiology-Breast Imaging and Beyond |Biomedgrid

Just as the adoption of full-field digital mammography (FFDM) over analog film improved breast cancer diagnoses in the early 2000s, and recent studies continue to suggest the efficacy of digital breast tomosynthesis (DBT) beyond FFDM even, the clinical signs thus far are pointing to artificial intelligence (AI) as the next frontier in breast cancer detection. Marrying unparalleled efficiency with an ever-improving accuracy often indistinguishable from its programmer, AI, machine learning, and predictive analytics provide a much-needed framework for patient education, while bolstering the entire enterprise of contemporary radiology, itself. Whereas AIsynched networks for breast imaging are currently limited in use in the United States, the practical need to integrate some aspect of automation into the screening environment remains. Distinct from the 2-dimensional mammogram with its average yield of 5 images, the granularity afforded by DBT results in much larger datasets, which, in turn, drastically increase the amount of time needed to analyze them.