Clairity’s approach is grounded in decades of work focused on the early identification of cancer. Review recent examples of research and news regarding Clairity and Clairity founder Dr. Connie Lehman, MD, PhD, related to computer aided diagnosis, deep learning, artificial intelligence, density assessment, predictive risk models, effective imaging workflow and risk-based triage.
Clairity, Inc., a company pioneering AI-powered risk prediction in human health, today announced that Founder Dr. Connie Lehman has been named to the Forbes “50 Over 50: Innovation” list.
Clairity Inc., a leader in AI-based breast cancer risk prediction, today announced that five scientific presentations featuring its FDA-authorized image-only AI model will be presented at the Radiological Society of North America (RSNA) 2025 Annual Meeting, taking place November 30 to December 4 in Chicago.
Health-tech companies are designing models that identify patients at risk of developing cancer, and who might need more screening or preventive care.
Clairity Breast is an AI-powered tool that is able to predict your risk for the disease in the next five years. Constance Lehman, M.D., Ph.D., the founder of Clairity Inc. and a Breast Cancer Research Foundation (BCRF) investigator, says that it employs deep learning, a form of AI that teaches computers to process data like the human brain.
A seismic shift is happening in how we understand and manage breast cancer risk. For decades, mammography was used primarily to detect cancer after it had already formed. But today, artificial intelligence (AI) and advanced imaging tools are enabling something once thought impossible: predicting cancer risk before it develops.
Breast cancer kills 42,000+ women in the USA annually. Connie Lehman, MD, PhD & Jeff Luber, JD, MBA of CLAIRITY, Inc. have received FDA authorization for the first A.I.-powered mammography tool – CLAIRITY BREAST – that predicts 5-year cancer risk. It will save many lives. For details go to Clairity.com
What if you could know your risk of developing breast cancer over the next five years? A Boston company is trying to do that using artificial intelligence.
BOSTON–(BUSINESS WIRE)–Clairity, Inc., advancing AI-powered human health solutions, today announced that members of its executive leadership team will be attending and participating in the upcoming BIO International Convention 2025, taking place June 16-19, 2025 in Boston, with its CEO, Jeff Luber, presenting the company update.
Clairity, Inc., a digital health innovator advancing AI-driven healthcare solutions, has received U.S. Food and Drug Administration (FDA) De Novo authorization for CLAIRITY BREAST, a novel, image-based prognostic platform designed to predict five-year breast cancer risk from a routine screening mammogram.
Clairity, Inc. is proud to announce a $1M investment from the Breast Cancer Research Foundation (BCRF) in support of Clairity, a leading innovator in AI-powered tools for breast cancer risk assessment.
Clairity, Inc., a pioneer in AI-based predictive tools for breast cancer, is pleased to announce the appointment of Jeffrey R. Luber, JD, MBA, as President, Chief Executive Officer and Director. Luber, an award-winning healthcare leader with extensive experience across oncology, genetic sequencing, infectious disease testing and digital health, will guide Clairity through market launch and global expansion.
Deep learning breast cancer risk models demonstrate improved accuracy compared to traditional risk models. This research highlights the promise deep learning models to support equitable, risk-based mammography screening.
Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.
The COVID-19 pandemic took a toll on mammography screening volumes among women of color, slashing exam uptake by more than 50% between 2019 and 2020.
Inconsistency in density assessment of mammograms has been widely recognized for the potential to cause patient anxiety and result in unnecessary procedures. Using a deep learning (DL) algorithm to assess mammographic breast density can reduce this inconsistency.
Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.
The development of an AI-based system for early detection of breast cancer has the ability to predict whether a patient is likely to develop the disease in the next five years. A technology that had not yet penetrated the hospital setting now has the potential to save thousands of lives each year.
Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick (version 8) model.
ML is having a huge impact in cancer diagnostics, specifically on the development of novel computational tools for stratification, grading, and prognostication of patients with the goal of improving patient care.
Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines.
Physicians still lack consensus on the meaning of race. When the Journal took up the topic in 2003 with a debate about the role of race in medicine, one side argued that racial and ethnic categories reflected underlying population genetics and could be clinically useful. Others held that any small benefit was outweighed by potential harms that arose from the long, rotten history of racism in medicine.
