While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist regarding when and how often they should be administered. On the one hand, advocates argue for the ability to save lives: Women aged 60-69 who receive mammograms, for example, have a 33 percent lower risk of dying compared to those who don't get mammograms. Meanwhile, others argue about costly and potentially traumatic false positives: A meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography. Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch-all: Women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind.
Artificial intelligence and machine learning systems continue to be adopted into an ever wider array of healthcare applications, such as assisting doctors with medical image diagnostics. Capable of understanding X-rays and rapidly generating MRIs -- sometimes even able to spot cases of COVID -- these systems have also proven effective at noticing early signs of breast cancer which might otherwise be missed by radiologists. Google and IBM, as well as medical centers and university research teams around the world, have all sought to develop such cancer-catching algorithms. They can spot worrisome lumps as well as radiologists can and predict future onsets of the disease "significantly" better than the humans that trained them. However many medical AI imaging systems produce markedly less accurate results for black and brown people -- despite WOC being 43 percent more likely to die from breast cancer compared to their white counterparts.
Despite major advances in genetics and modern imaging, the diagnosis catches most breast cancer patients by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, uncertain outcomes, and more medical expenses. As a result, identifying patients has been a central pillar of breast cancer research and effective early detection. With that in mind, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future.
Breast cancer is the global leading cause of cancer-related deaths in women, and the most commonly diagnosed cancer among women across the world (1). From our perspective, improved treatment options and earlier detection could have a positive impact on decreasing mortality, as this could offer more options for successful intervention and therapies when the disease is still in its early stages. Our team of IBM researchers published research in Radiology around a new AI model that can predict the development of malignant breast cancer in patients within the year, at rates comparable to human radiologists. As the first algorithm of its kind to learn and make decisions from both imaging data and a comprehensive patient's health history, our model was able to correctly predict the development of breast cancer in 87 percent of the cases it analyzed, and was also able to correctly interpret 77 percent of non-cancerous cases. Our model could one day help radiologists to confirm or deny positive breast cancer cases.
Despite significant advancements in genetics and modern imaging technology, for the vast majority of breast cancer patients, the diagnosis catches them by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, anxiety and uncertain outcomes. Therefore, identifying patients at risk before the disease develops has been a central pillar to breast cancer research and effective early detection programs. A team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep learning model that can predict from a mammogram if a patient is likely to develop breast cancer in the future.