The era of one-size-fits-all breast cancer screening is ending — and radiomics and artificial intelligence are driving what comes next. In this session, Dr. Connie Lehman presents the scientific and clinical frontier of precision breast health, exploring how AI-powered analysis of imaging data can extract complex biological signals invisible to the human eye, generate individualized risk predictions, and support more precise clinical decision-making across the breast care continuum. From deep learning models that estimate five-year cancer risk from a single mammogram to radiomic biomarkers that complement genomic data, this session offers clinicians a rigorous, forward-looking review of where the technology stands, where it is headed, and what it means for screening, counseling, and care for women at risk.

Connie Lehman, MD, PhD, is Professor of Radiology at Harvard Medical School and Chief of Breast Imaging and Co-Director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital. A physician-scientist with MD and PhD degrees from Yale University and undergraduate training at Duke University, Dr. Lehman is internationally recognized as a pioneer in the clinical implementation of artificial intelligence in breast imaging. Her research applies deep learning, radiomics, and advanced imaging analytics to improve breast cancer detection, individualize risk assessment, and move the field from age-based toward precision, risk-based screening. She has authored more than 300 peer-reviewed publications and has served as Principal Investigator on numerous clinical trials in breast MRI, mammography, and ultrasound. Dr. Lehman has co-authored breast cancer screening guidelines for the American Cancer Society, the American College of Radiology, and the National Comprehensive Cancer Network, and serves on the National Cancer Institute's Breast Cancer Steering Committee. She has collaborated extensively with MIT on AI-driven risk modeling and is a leading voice in translating imaging innovation into equitable, patient-centered clinical practice.