TBD
For most of medical history, a woman’s breast cancer risk was calculated the same crude way: Did her mother have it? Her sister? Did she carry a known genetic mutation? If the answers were no, she was told, essentially, don’t worry. Come back at 40, then see you every year or two.
The trouble is that those family-history and genetic models only ever captured about 15 percent of the women who would go on to be diagnosed. The remaining 85 percent were, in medical terms, “sporadic,” meaning cancer might appear with no visible warning sign, no glaring red flag on the chart. “We didn’t have a way to find those 85 percent in advance,” says Dr. Constance Lehman, a professor of radiology at Harvard Medical School and a breast imaging specialist who has spent her career at the bleeding-edge of radiology research. “Until now.”
Dr. Lehman’s aha moment occurred in 2016, when she moved to Boston and began collaborating with a colleague at Massachusetts Institute of Technology on imaging and machine learning. Radiologists had spent decades training computers to spot a mass, a calcification, or a lesion, all cancer indicators humans had long been trained to see. Dr. Lehman wondered if, with the rapid advancements in artificial intelligence, a machine could look at a woman’s mammogram today and predict whether or not she was likely to develop cancer in five years.
Turns out, it could. By feeding the model thousands of mammograms from women who later developed cancer alongside thousands from women who never did, Dr. Lehman built an algorithm that could detect faint biological “signals”— subtle patterns in breast tissue shaped by body mass index, hormonal history, pregnancy, breastfeeding, menopause, and other factors—so faint that no radiologist could reliably read them, but that were, in effect, written onto the tissue itself. “All of those weak signals that people had studied… those biological events are laid down in the breast tissue,” Dr. Lehman explains. “The AI can extract those predictive signals.”
That early research—the model Dr. Lehman and her team called Mirai—was built with support from the Breast Cancer Research Fund. BCRF’s involvement was, by Dr. Lehman’s account, the reason any of what followed became possible. “None of this would have happened without BCRF,” she says, holding back tears as she explains the breakthrough and the constant stream of funding it required to bring an outlandish idea to reality. When Dr. Lehman decided to leave the lab and build a company, BCRF went further, with the organization’s board and CEO, Donna McKay, pledging its first venture philanthropy investment in this early, game-changing product. The result was Clairity, a for-profit company, built on a genuinely global dataset spanning the US, South America, and Europe. This corrected what had become an established blind spot in AI medicine: Roughly 95 percent of health care AI models had historically been trained on patients from just three states.
In what may be one of the fastest clearances in medical history, in May 2025, Clairity received FDA de novo authorization—meaning the drug regulator recognized the tool as a novel pathway to minimizing cancer, and wanted it raced to market. Three months later, it was submitted to the National Comprehensive Cancer Network (NCCN), and by 2026 it had been folded into national screening guidelines, which also changed the recommended baseline breast cancer risk assessment from age 40 to age 35, also a major win, since breast cancer patients have been trending younger (that is, an alarming rise in women under 50 receiving the devastating diagnosis) since 2012, according to the Centers for Disease Control and Prevention. Every woman screened with Clairity receives a score that represents her individualized five-year breast cancer risk. Any score above 1.7 percent qualifies a patient for enhanced screening; above 3 percent, and she’s classified as high risk, which triggers supplemental imaging and risk-reduction conversations around anything from diet, exercise, and alcohol consumption to taking preventative medications to pre-emptive mastectomies, that, until now, were reserved almost exclusively for women with a strong family history or known genetic mutation.
The rollout is moving on multiple fronts at once, from individual hospital systems to a national imaging-center collective to a consumer offering through digital health company Everlywell that will let women request their own risk score directly simply by uploading their 2D/3D scans. (The latter is expected to be rolled out before Breast Cancer Awareness Month, this October). Next comes the cost to patients: Clairity is working with the American Medical Association and the American College of Radiology to secure a CPT code, which makes broad insurance coverage feasible, by 2027.
What excites Dr. Lehman most isn’t just earlier detection—which is a big deal—it’s what becomes possible once breast cancer risk itself is something doctors can measure and track. Because scores shift over time, researchers can now study how lifestyle and medication actually change a woman’s biological risk, including a planned study into whether GLP-1 drugs, already linked to a roughly 30 percent reduction in breast cancer risk, measurably alter breast tissue itself. They are hoping to reverse the two-decade trend of women between the ages of 30 and 39 seeing nearly a 20 percent increase in breast cancer. Dr. Lehman and her team are even exploring whether the same tissue signals could one day predict cardiovascular risk.
For a disease that affects one in eight women, the shift underway is profound, from reacting to cancer once it appears, to seeing it coming and, increasingly, preventing it altogether. “The whole field of cancer prevention has just opened up,” says Dr. Lehman.
