IBM tests the use of artificial intelligence for breast cancer screenings ZDNet

A recent study by IBM Research, together with Sage Bionetworks, Kaiser Permanente Washington Health Research Institute, and the University of Washington School of Medicine, has uncovered how combining machine learning algorithms and assessments by radiologists could improve the overall accuracy of breast cancer screenings. Mammogram screenings, commonly used by radiologists for the early detection of breast cancer, according to IBM researcher Stefan Harrer, frequently rely on a radiologist's expertise to visually identify signs of cancer, which is not always accurate. "Through the current state of human interpretation of mammography images, two things happen: Misdiagnosis in terms of missing the cancer and also diagnosing cancer when it's not there," Harrer told ZDNet. "Both cases are highly undesirable -- you never want to miss a cancer when it's there, but also if you're diagnosing a cancer and it's not there, it creates enormous pressure on patients, on the healthcare system, that could be avoided. "That is exactly where we aim to improve things through the incorporation of AI (artificial intelligence) to decrease the rate of false positives, which is the diagnosis of cancer, and also to decrease missing the cancer when there is one." The research used more than 310,800 de-identified mammograms and clinical data from Kaiser Permanente Washington (KPWA) and the Karolinska Institute (KI) in Sweden. Of the combined datasets, KI contributed around 166,500 examinations from 6,800 women, of which 780 were cancer positive; while the remaining 144,200 examinations were provided by KPWA from 85,500 women, of which 941 were cancer positive. "We had hundreds of thousands of mammograms that were annotated.