Senior medical analyst Dr. Marc Siegel discusses advancements in artificial intelligence aimed at predicting an individuals future risk of breast cancer and the increased health risks from cannabis as users age. SMARTER SCREENINGS: The U.S. Food and Drug Administration (FDA) has approved the first artificial intelligence (AI) tool to predict breast cancer risk. NOVA IN ACTION: Flock Safety has released another piece of revolutionary technology aimed at keeping everyday civilians safe from crime. The company's new product, Flock Nova, helps law enforcement with a common but often overlooked problem – a lack of data sharing and access. ROBOT NURSES RISING: The global healthcare system is expected to face a shortage of 4.5 million nurses by 2030, with burnout identified as a leading cause for this deficit.

Senior medical analyst Dr. Marc Siegel discusses advancements in artificial intelligence aimed at predicting an individual's future risk of breast cancer and the increased health risks from cannabis as users age. The U.S. Food and Drug Administration (FDA) has approved the first artificial intelligence (AI) tool to predict breast cancer risk. The authorization was confirmed by digital health tech company Clairity, the developer of Clairity Breast – a novel, image-based prognostic platform designed to predict five-year breast cancer risk from a routine screening mammogram. In a press release, Clairity shared its plans to launch the AI platform across health systems through 2025. Most risk assessment models for breast cancer rely heavily on age and family history, according to Clairity.

A biopsy that turns out to have benign results can be a relief. But in some cases, it could also mean a patient whose risk of cancer was low from the start has gone through an unnecessarily invasive procedure. By and large, radiologists recommend that patients whose breast MRI scans raise suspicion of a cancerous growth get a biopsy done. But MRIs often pick up on benign lesions that other mammograms and ultrasound may not. This leads to some patients having their lesions falsely classified as higher risk than they are, and undergoing a biopsy.

Compared with commonly used clinical risk factors, a sophisticated type of artificial intelligence (AI) called deep learning does a better job distinguishing between the mammograms of women who will later develop breast cancer and those who will not, according to a new study in the journal Radiology. Researchers said the findings underscore AI's potential as a second reader for radiologists that can reduce unnecessary imaging and associated costs. Annual mammography is recommended for women starting at age 40 to screen for breast cancer. Research has shown that screening mammography lowers breast cancer mortality by reducing the incidence of advanced cancer. Mammograms not only help detect cancer but also provide a measure of breast cancer risk through measurements of breast density.

Researchers have developed a deep learning model that identifies imaging biomarkers on screening mammograms to predict a patient's risk for developing breast cancer with greater accuracy than traditional risk assessment tools. Traditional risk assessment models do not leverage the level of detail that is contained within a mammogram," said study author Leslie Lamb from the Massachusetts General Hospital (MGH) in the US. "Even the best existing traditional risk models may separate sub-groups of patients but are not as precise on the individual level," Lamb added. Currently available risk assessment models incorporate only a small fraction of patient data such as family history, prior breast biopsies, and hormonal and reproductive history. Only one feature from the screening mammogram itself, breast density, is incorporated into traditional models.

This week, 16 teams of teens from around the world assembled in Mountain View to demonstrate the results of research projects at the Google Science Fair. You can view summaries of all the projects here. I've been attending these finals for several years now and am always impressed with how creatively the teens use the technologies of today. And this year was no exception: machine learning is hot in the tech world, and the teens are embracing it. A Silicon Valley girl from Cupertino, Calif., Cheerla was curious about the current state of breast cancer prediction, and discovered that prediction methods using digital mammograms are just 64 percent effective, typically simply considering the percentage of dense tissue in a breast.

An artificial intelligence software developed that can assist in predicting breast cancer risk.