Background

Catching breast cancer early, when treatment is most effective and survival rates are highest, depends on identifying changes that are often subtle and easy to miss. Traditional mammography screening relies on human interpretation, which varies between radiologists and is subject to fatigue and inconsistency.

Our AI system achieves 98.57% accuracy in breast cancer detection from mammography images, with 99.05% sensitivity and 98.10% specificity. The underlying physics-inspired neural network analyzes patterns at multiple scales, picking up indicators that conventional analysis frequently overlooks. Earlier detection means treatment begins at the point where it does the most good. Performance exceeds current industry benchmarks. All training data is publicly available and meets HIPAA guidelines.