Google Improves Breast Cancer Screening Using AI

Google's AI model spotted breast cancer in de-identified screening mammograms with highr accuracy.

For last two years, Google team have been working with colleagues at DeepMind, Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital on a model that was trained and tuned on a representative data set comprised of de-identified mammograms from more than 75000 women in the U.K. and over 15000 women in the U.S., to see if it could learn to spot signs of breast cancer in the scans.
The model was then evaluated on a separate de-identified data set of more than 25,000 women in the U.K. and over 3,000 women in the U.S.In this evaluation, the model produced a 5.7 percent reduction of false positives in the U.S and a 1.2 percent reduction in the U.K. It produced a 9.4 percent reduction in false negatives in the U.S. and a 2.7 percent reduction in the U.K.
Google team also wanted to see if the model could generalize to other healthcare systems as well. To do this, they trained the model only on the data from the women in the U.K. and then evaluated it on the data set from women in the U.S. In this separate experiment, there was a 3.5 percent reduction in false positives and an 8.1 percent reduction in false negatives, showing the model’s potential to generalize to new clinical settings while still performing at a higher level than experts.
During this experiment, the only information that the model had was that mammograms of the patients, while human experts had access to a no of other information like, patient's history, prior mammograms.
Despite working from these X-ray images alone, the model surpassed individual experts in accurately identifying breast cancer.
Looking forward to future applications, there are some promising signs that the model could potentially increase the accuracy and efficiency of screening programs, as well as reduce wait times for patients.