Google AI Beats Experts at Breast Cancer Detection: New Study

Google AI Beats Experts at Breast Cancer Detection
According to the recent study by the researchers in the United States and the United Kingdom an artificial intelligence (AI) system from Google proved as good as expert radiologists at detecting which women had breast cancer based on screening mammograms – and that the system showed promise at reducing errors.


Although breast cancer is the most common type of cancer among women, detection is difficult due to high rates of false positives which cause distress and can lead to unnecessary medical interventions.

Google Health worked together with Cancer Research UK Imperial Centre, Northwestern University, Royal Surrey County Hospital and DeepMind, Google parent Alphabet Inc.’s AI development unit. The machine learning model (AI) has taught to identify cancerous tissue by training it on anonymized mammograms from more than 91,000 women in the U.S. and the U.K. Then, Google put the neural network to the test by having it analyze a separate dataset of scans from 28,000 patients. The model managed to detect tumours with a lower error rate than a panel of six radiologists who took part in the study.

Unlike human experts, who used patient histories and prior mammograms to make their assessments, the AI only had access to the most recent mammogram of each patient. “Notably, when making its decisions, the model received less information than human experts did,” Google Health researchers Shravya Shetty and Daniel Tse wrote in a blog post.

Study co-author Dr Mozziyar Etemadi, from Northwestern University, Chicago, said: “This is a huge advance in the potential for early cancer detection.

“Breast cancer is one of the highest causes of cancer mortality in women. Finding cancer earlier means it can be smaller and easier to treat. We hope this will ultimately save a lot of lives.”

Google believes that its AI has the potential to ease early detection as well as reduce wait times for patients by freeing up human medical staff’s time.