Breast cancer screening has long been governed by age thresholds that treat a 40-year-old with dense tissue and a family history identically to one without those risks. A systematic review now synthesizes the most rigorous evidence yet on whether AI can finally break that blunt instrument — and the answer is a qualified yes, with important caveats attached.
Drawing on 30 studies selected from 612 records across five major databases (2015–2025), the review finds that deep-learning models trained on mammographic images consistently outperformed legacy clinical risk calculators such as Tyrer-Cuzick and IBIS in stratifying women by future cancer probability. Crucially, these AI systems showed particular strength in flagging interval cancers — tumors that slip through standard screening cycles — a historically stubborn problem. On the workflow side, prospective trials and real-world deployments showed AI-assisted reading maintained or slightly improved cancer detection rates while reducing radiologist reading burden by approximately 40–50%, a finding with direct implications for overstretched radiology departments globally. Decision-analytic modeling further suggests that AI-enabled risk-stratified screening policies could be cost-effective compared with uniform annual or biennial schedules, though these models rest on assumptions not yet confirmed by long-term prospective data.
This review arrives at a pivotal moment. Several randomized trials of AI-assisted mammography — including the ScreenTrust CAN and Transpara studies — have already altered clinical debate in Europe, and regulatory clearances for AI reading aids are accelerating in the US and UK. What distinguishes this synthesis is its explicit attention to equity: the authors flag that training datasets skewed toward certain demographics risk amplifying existing disparities in missed diagnoses among women with denser or darker-pigmented breast tissue. The review is narrative rather than meta-analytic, meaning pooled effect sizes are unavailable and heterogeneity across study designs limits directness. Most included studies are observational or retrospective; only a minority are prospective randomized trials. Still, the convergence across independent implementations is notable. For health systems weighing personalized screening protocols, this represents strong directional evidence — though confirmation from ongoing large-scale trials remains essential before wholesale policy adoption.