CoActiv tailors PACS for breast imaging

In a study involving over 24,000 women, two-thirds of whom had dense breasts, adjunctive AI led to a significantly higher rate of cancer detection for breast radiologists with no difference in recall rates.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

Black, Hispanic, and Asian women were over 25 percent less likely than White women to have same-day follow-up diagnostic service after abnormal findings on screening mammography exams, according to a new study involving over one million patients.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

Use of the mammography AI software had a nearly equivalent false positive rate as unassisted radiologist interpretation and resulted in a 44 percent reduction in screen reading workload, according to findings from a randomized controlled trial involving over 105,000 women.

For women with intermediate risk and a personal history of breast cancer, an emerging AI system offered an 81 percent AUC for breast cancer detection, according to new research.