Enhancing breast cancer detection with AI for early diagnosis and recurrence prediction

ABSTRACT

Breast cancer remains one of the leading causes of death among women worldwide, early detection is critical for improving survival. Conventional screening modalities such as mammography, MRI, biopsy, and ultrasound have been pivotal for patient care, but face limitations, including human variability, interpretive errors, operator dependence, and high rates of false positives and negatives which can lead to unnecessary treatment and financial distress. To overcome these challenges, a more accurate and predictive diagnostic tool is needed. This article compares traditional breast cancer screening methods with AI-integrated approaches demonstrating how artificial intelligence enhances diagnostic precision, efficiency, and predictive capability. AI-assisted mammography has been shown to detect 29% more cancers than traditional mammography without increasing false positives and reduces radiologist reading time by 40%. AI-assisted 3D digital breast tomosynthesis detects 1.6 additional cancers per 1,000 screenings and reduces recall rates by 2.2% compared to 2D mammography. In MRI, AI predicted breast cancer development up to one year in advance and correctly localize future cancer sites in 57% of cases. In ultrasound, AI significantly improves diagnostic accuracy, particularly for less experienced radiologists. In biopsy, AI-enabled digital assays for recurrence prediction achieve higher predictive accuracy, with pathologists using AI being 62% faster and 72% more accurate. Integrating AI into breast cancer redefines diagnostic excellence, shifting breast cancer management toward a more proactive, precise, and patient-centered approach. Future research should focus on external validation of AI models across larger and diverse populations, cost-effectiveness to ensure equitable access and ethical consideration to ensure responsible clinical adoption.