Researchers at Karolinska Institutet and Karolinska University Hospital in Sweden have compared the ability of three artificial intelligence (AI) algorithms to identify breast cancer based on previously taken mammograms. The best algorithm proved to be as accurate as the average radiologist. The results, published in JAMA Oncology, may lead the way in reorganising breast cancer screening for the future. "This is the first independent comparison conducted to assess the accuracy of several different AI algorithms," says study author Fredrik Strand, a researcher at the Department of Oncology-Pathology at Karolinska Institutet and a radiologist at Karolinska University Hospital. "We can demonstrate that one of the three algorithms is significantly better than the others and that it equals the accuracy of the average radiologist."

A doctor looks at the results of a mammography. Despite all of our fears about artificial intelligence (AI), we still have to look at the health benefits that could save lives through machine learning. According to a study conducted by researchers from Imperial College London and Google Health, an AI program has been developed that can identify breast cancer from routine scans with greater accuracy than its human counterparts. Google's DeepMind system has been trained to spot abnormalities on X-ray images. According to the findings, which were published this week by Nature, scans of 29,000 women from the U.S. and UK were used in the trial.

Screening mammography aims to identify breast cancer before symptoms appear, enabling earlier therapy for more treatable disease. Despite the existence of screening programs worldwide, interpretation of these images suffers from suboptimal rates of false positives and false negatives. Here we present an AI system capable of surpassing a single expert reader in breast cancer prediction performance. Using two large data sets representative of clinical practice in the United States (US) and the United Kingdom (UK), we show an absolute reduction of 5.7%/1.2% We show evidence of the system's ability to generalize from the UK sites to the US site.

Authors from the Center for Data Science at New York University were Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Thibault Févry, and Kyunghyun Cho, who is also on the faculty of NYU's Courant Institute of Mathematical Sciences. Also authors were Kara Ho at SUNY Downstate College of Medicine; Masha Zorin in the Department of Computer Science and Technology at the University of Cambridge in the United Kingdom; and Stanisław Jastrzębski from Jagiellonian University in Poland, and Joe Katsnelson in the Department of Information Technology, NYU Langone Health.