Overview
AI assists in mammography tomosynthesis and MRI by highlighting suspicious findings and estimating cancer risk. Risk stratification models combine imaging features with clinical data to personalize screening intervals. Validation and equity testing are required to avoid bias in risk estimates.
Detection and Triage
AI can improve cancer detection and reduce recall rates when integrated with radiologist review. Triage tools prioritize worklists for likely positive studies to shorten time to diagnosis. False positives and algorithmic bias must be managed through threshold tuning and oversight.
Risk Models and Screening
Imaging based risk models can identify patients who benefit from supplemental MRI or shorter screening intervals. Incorporate genetic family history and breast density for comprehensive risk assessment. Shared decision making with patients supports personalized screening plans.
Implementation
Deploy AI with structured reporting and clear documentation of algorithm role and performance. Monitor outcomes across demographic groups to ensure equitable benefit. Engage multidisciplinary teams including breast surgeons and genetic counselors for integrated care.