Massachusetts Institute of Technology
To catch cancer earlier, we need to predict who is going to get it in the future. The complex nature of forecasting risk has been bolstered by artificial intelligence (AI) tools, but the adoption of AI in medicine has been limited by poor performance on new patient populations and neglect to racial minorities.
Two years ago, a team of scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic (J-Clinic) demonstrated a deep learning system to predict cancer risk using just a patient’s mammogram. The model showed significant promise and even improved inclusivity: It was equally accurate for both white and Black women, which is especially important given that Black women are 43 percent more likely to die from breast cancer.
January 27th, 2021
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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.
MIT
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