Breast cancer is a condition that affects far too many women across the globe. More than 55,000 people in the U.K. are diagnosed with breast cancer each year, and about 1 in 8 women in the U.S. will develop the disease in their lifetime.
Digital mammography, or X-ray imaging of the breast, is the most common method to screen for breast cancer, with over 42 million exams performed each year in the U.S. and U.K. combined. But despite the wide usage of digital mammography, spotting and diagnosing breast cancer early remains a challenge.
Reading these X-ray images is a difficult task, even for experts, and can often result in both false positives and false negatives. In turn, these inaccuracies can lead to delays in detection and treatment, unnecessary stress for patients and a higher workload for radiologists who are already in short supply.
Over the last two years, we’ve been working with leading clinical research partners in the U.K. and U.S. to see if artificial intelligence could improve the detection of breast cancer. Today, we’re sharing our initial findings, which have been published in Nature. These findings show that our AI model spotted breast cancer in de-identified screening mammograms (where identifiable information has been removed) with greater accuracy, fewer false positives, and fewer false negatives than experts. This sets the stage for future applications where the model could potentially support radiologists performing breast cancer screenings.
