- Understand and contextualize the history of breast cancer risk assessments and how new IBR technologies are being developed
- Evaluate the differences in performance between traditional risk assessments and IBR models
- Discuss the evolving landscape for IBR assessment, including the 2026 NCCN guidelines, and the potential implications for breast cancer screening practice
- Translate the core parts of a mammogram report, including BI-RADS categories, breast density, and common descriptive language, into words a patient can act on.
- Pinpoint where in a report patient anxiety usually starts, and why templated patient handouts don't resolve it.
- Determine whether a patient-facing explanation layer added to an existing report and workflow would save time in their own practice.
- Explore how AI-derived risk scoring may facilitate a more dynamic assessment of long-term breast cancer risk
How AI is Reinventing Risk Stratification and Reporting for Mammography Exams
About The Event
The mammogram has always been central to breast cancer screening, but it is now doing far more than detecting disease. New AI models can use a woman's existing mammogram to estimate her future breast cancer risk, often more accurately than traditional models that depend on clinical and family history that may be incomplete or hard to collect. At the same time, patients are reading their own reports the moment they're signed, frequently before a clinician has had the chance to explain them. This event brings together leading voices on both fronts. AI researcher and image-based risk (IBR) model developer Andreas Lauritzen will trace how breast cancer risk assessment has evolved, compare the performance of IBR models with traditional approaches, and discuss what the 2026 NCCN guidelines mean for screening practice. Connie Lehman, MD, PhD, will share recent research on AI-derived five-year risk scores and how they can sharpen risk stratification. Dr. Allison Rubin will then walk attendees through a real mammogram report as a patient sees it, decode the terms that most often cause confusion and alarm, and share how Expert Radiology built a plain-English explanation layer tied to each patient's own images and report. Attendees will leave with a clearer view of where AI-driven risk assessment is headed and practical ways to help patients understand what their mammograms are telling them.
Image-based Risk: What You'll Learn
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