The impact of artificial intelligence technologies on active breast cancer detection: efficiency and scalability

Abstract

Relevance. In the Russian Federation, breast cancer is the most common cancer among women, with the highest incidence and mortality rate (approximately 15.5%). Mammography is the standard method for mass screening aimed at early detection of malignant breast tumors and ensuring timely and optimal treatment outcome. The purpose of the study : to evaluate the effectiveness of double reading of mammograms using artificial intelligence (AI) technologies. Materials and methods. An epidemiological study was conducted from 2015 to 2024 using the URIS UMIAS, annual reference books, and federal statistical monitoring forms. In 2023, double reading for mammography using AI was developed and implemented based on data from the Moscow Experiment. Results. Comparison of active breast cancer detection rates revealed distinct dynamics. In Moscow, detected cases increased from 40.9% (2015) to 52.3% (2023), with a pandemic-induced decline to 18.5% (2021) and post-COVID recovery at 9.6%/year. Nationwide, rates rose steadily from 37.2% to 44% (2.5%/year), without significant contrasts. The introduction of double reading of mammography AI decisively contributed to the significant, sustainable increase in active detection of breast cancer patients. Conclusion. Artificial intelligence-enabled medical devices are recommended for transforming the double reading of mammography. Long-term implementation ensures a significant, sustainable increase in active detection of women with malignant breast tumors.

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