Ross Shachter: Can AI improve mammography?

Radiologists have the difficult job of detecting and diagnosing malignant tumors. A new computer model could improve their accuracy. In breast cancer pathology, a 2 percent chance of malignancy is the accepted threshold at which a radiologist refers the patient for further study. In reality, that threshold varies among doctors; some are more conservative, others less so. The result is either more false positives, in which a healthy patient worries unnecessarily they have cancer, or more-worrisome false negatives, in which a patient is told they are fine when they are not. One researcher working to reduce that gap is Stanford’s Ross Shachter. He is a professor of management science and engineering and an expert in using probability to improve decision making. Though Shachter is an engineer, he applies his approaches not to operational efficiency or business management, but to the high-stakes field of mammography, where decisions often have life or death consequences. He says that probability and decision making theory could be integrated into artificial intelligence applications that could help doctors better evaluate patient options, outcomes and preferences to improve care. Join host Russ Altman and Ross Shachter for a look at how engineering and AI are changing the world of breast cancer diagnosis. You can listen to The Future of Everything on Sirius XM Insight Channel 121, iTunes, Google Play, SoundCloud, Spotify, Stitcher or via Stanford Engineering Magazine.