In a recent study published in the journal Radiology , researchers in Denmark and the Netherlands conducted a retrospective analysis of the screening performance and overall workload associated with mammography screening before and after implementing artificial intelligence (AI) screening systems. Study: Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer . Image Credit: Radiological imaging / Shutterstock Regular mammography-based screening for breast cancer has been found to decrease the mortality rates for breast cancer significantly.
However, population-based mammography screening results in a substantial increase in workload for the radiologists who have the task of reading numerous mammograms, most of which do not indicate any suspicious lesions. Furthermore, the process of double screening to lower the rate of false positives and improve the detection rates further compounds the workload for radiologists. The dearth of specialized radiologists for reading mammograms exacerbates the already heavy workload.
Recent studies have extensively examined the use of AI in efficiently screening radiology reports while maintaining high screening performance standards. A combined approach where AI tools are used to assist radiologists in narrowing down mammograms with lesion markings is also believed to decrease the workload for radiologists while maintaining screening sensitivity. The present study used preliminary performance indicators from two .
