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 co-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.
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.
Purpose: To develop a deep learning (DL) algorithm to assess mammographic breast density.
Published 27 January 2021, Sci. Transl. Med
Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency.
When artificial intelligence researcher Regina Barzilay was first diagnosed with breast cancer in 2014, she says she was struck by immediate questions: “Am I going to survive? What’s going to happen to my son?”
Deep learning models that use full-field mammograms yield substantially improved risk discrimination compared with the Tyrer-Cuzick (version 8) model.
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.