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Constance D. Lehman, MD, PhD

Massachusetts General Hospital
Boston, Massachusetts

Titles and Affiliations

Professor of Radiology, Harvard Medical School
Chief of Breast Imaging and Co-Director of AVON Breast Center

Research area

Using advanced tools of artificial intelligence to develop methods to improve breast cancer screening and identify women most at risk.


The first step in breast cancer prevention is an accurate assessment of individual risk so that personalized prevention strategies can be implemented. Breast mammography has served as the gold standard for breast cancer screening. However, mammography and other imaging technologies used in health care require highly specialized training and are also subject to human interpretation. Furthermore, they each have limitations that can either result in false positives (reporting a cancer that is not there), or false negatives (reporting no cancer when, in fact, cancer is present). With the rapid advances in artificial intelligence (AI), there is a great opportunity to improve upon current age-based risk models, which are limited in scope for both personalized risk assessment and predicting risk of specific breast cancer subtypes. Dr. Lehman and her multidisciplinary team of scientists use AI to extract complex information contained within a breast image—such as breast density—to develop risk models for breast cancer. The shift from age-based to risk-based screening will benefit significantly from the predictive power of AI with the potential application to different cancer types and patient populations.

Progress thus far

Dr. Lehman and her team created an advanced breast cancer risk prediction model combining imaging, biologic, and behavioral data to personalize screening regimens and improve early detection while lowering the overall cost of screening. They confirmed that advanced AI methods applied to mammogram images support more accurate breast cancer risk assessment across diverse races and ethnicities as compared to traditional risk models. Importantly, Dr. Lehman’s AI models do not suffer from the racial biases that have been identified in traditional risk assessment models.

What’s next

Dr. Lehman’s team is currently preparing a merged data set, which includes data about reproductive factors, established risk factors such as family history of breast cancer, and factors like BMI, height, and weight. The team is also incorporating circulating biomarker data, genetic risk score, and other established breast cancer risk prediction scores to continue to improve personalized screening regimens and early detection. Leveraging the resources of their large Breast Imaging Data Repository Archive, a data repository that includes screening mammograms, cancer outcomes, and AI risk scores over a 13-year time span, Dr. Lehman will compare performance and outcomes of a generic age-based screening platform to a screening program based on patient risk assessed at each mammography encounter.


Constance “Connie” Lehman, MD, PhD is a Professor of Radiology at Harvard Medical School, Chief of Breast Imaging, and co-Director of the Avon Comprehensive Breast Evaluation Center at the Massachusetts General Hospital.  She is a physician scientist who received her MD and PhD from Yale University after graduating Phi Beta Kappa from Duke University. She is a change agent, innovator and pioneer in the domain of Artificial Intelligence implementation in clinical medical practice. Her current work applies artificial intelligence and advanced methods of deep learning to improve breast cancer detection, diagnosis and treatment. Her current efforts focus on applying AI to deliver higher-quality health outcomes at lower costs for the full diversity of patients at risk for and with cancer. This is a continuation of her career-long commitment to expand access for all patients globally to highest quality, patient-centered, affordable care.

With over 250 peer-reviewed scientific publications, she has led careful studies of advanced imaging tools to identify breast cancer at its earliest stages—when it can be cured. Collectively, her philosophy embodies the notion that we improve the health of our community by delivery the highest quality patient-centered care in a setting of active innovation and education.

In her prior leadership roles at the University of Washington and Seattle Cancer Care Alliance, she developed an internationally-recognized program of excellence in patient care, education and research. She collaborated with partners in industry, development, and multiple clinical and basic science disciplines to foster new opportunities for faculty to pursue their goals. As division chief of breast imaging and co-Director of the AVON Comprehensive Breast Center at MGH, she rebuilt the service care model as well as the education and clinical research programs to stimulate new growth and productivity in times of scarce resources.

In her career, Dr. Lehman has developed novel patient care pathways, which substantially improve efficiency and increase access for patients and increase satisfaction of patients, faculty and staff. Her methods are used widely by her former fellows and faculty, many who lead their own breast imaging programs.

Her research in clinical applications of breast MRI shaped the American Cancer Society and NCCN recommendations for screening MRI in high-risk patients. Dr. Lehman’s research on breast ultrasound updated the ACR appropriateness guidelines for women under 40, and this work continues to support best practices worldwide for women with palpable breast lumps.

Dr. Lehman has inspired many others to pursue careers in academic medicine and has served on a number of key national committees, including the National Cancer Institute’s Breast Cancer Steering Committee and the American College of Radiology’s (ACR) Committee on Breast Imaging for Appropriateness Criteria and Guidelines. She has given over 400 national and over 60 international invited talks spanning diverse topics in advanced imaging for more effective diagnosis and treatment of breast cancer.

BCRF Investigator Since


Donor Recognition

The Lampert Foundation Award