A 2023 systematic review of 16 studies found that the median area under the receiver operating characteristic curve (AUC) for AI imaging tools was 0.72, compared to 0.61 for breast density or clinical risk factor-based tools. Of note, in 7 of 8 studies that compared AI risk prediction to assessments that combined AI with clinical risk factors, the latter did not produce more accurate risk estimates than AI alone.
In a later study of an AI model that utilized retrospective data from more than 300,000 patients aged 50 to 70 years who had screening mammograms in the United Kingdom, the overall AUC was 0.7, with no differences in performance across ethnic or age groups. Similarly, in a recent U.S. study, a deep learning model involving mammograms from more than 67,000 patients produced an AUC of 0.71, compared to an AUC of 0.53 for breast density.
Last year, the U.S. Food and Drug Administration approved an AI test that analyzes a single screening mammogram performed within the past 12 months to classify patients as at average (less than 1.7%), intermediate (1.7% to 3%), or high (greater than 3%) risk of developing breast cancer over the next 5 years. Everlywell, a digital health company, is charging $249 for this test, which does not require a doctor’s order and is not covered by insurance. Patients are directed to share their report with their primary care clinicians to discuss next steps, which for those at intermediate or high risk could include more intensive screening or risk-reducing medications.
The trouble is, we don’t know if more accurate cancer risk prediction improves patient-oriented outcomes. Previous trials of risk-reducing medications such as tamoxifen and raloxifene used eligibility thresholds based on clinical risk assessments and showed reductions in breast cancer diagnoses, but not breast cancer deaths. Lifestyle modifications such as increasing physical activity, healthy eating patterns, and limiting alcohol consumption also reduce breast cancer risk, but whether women with increased risk will be more motivated to adopt these behaviors is unclear. In the absence of such evidence, AI tools may only increase preventive care costs without producing health benefits.








