Conventional breast cancer screening programs are predominantly age-based, applying uniform intervals and modalities across broad populations. While this model has reduced mortality, it entails harms—including overdiagnosis, false positives, and missed interval cancers—prompting interest in risk-stratified approaches. In recent years, artificial intelligence (AI) has emerged as a critical enabler of this paradigm shift. This narrative review examines how AI-driven tools are advancing breast cancer screening toward personalization, with a focus on mammographic risk models, multimodal risk prediction, and AI-enabled clinical decision support. We reviewed studies published from 2015 to 2025, prioritizing large cohorts, randomized trials, and prospective validations. AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across subtypes and populations. Emerging multimodal models integrate genetics, clinical data, and imaging; AI is also being evaluated for triage and personalized intervals within clinical workflows. Barriers remain—explainability, regulatory validation, and equity. Widespread adoption will depend on prospective clinical benefit, regulatory alignment, and careful integration. Overall, AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across molecular subtypes, with signals strongest for ER-positive disease and limited data for fast-growing and interval cancers. Prospective trials demonstrating outcome benefit and safe interval modification are still pending. Accordingly, adoption should proceed with safeguards, equity monitoring, and clear separation between risk prediction, lesion detection, triage, and decision-support roles

Background/Objectives: Vacuum-assisted excision (VAE) is a minimally invasive technique for breast tumor treatment, offering precision, comfort, and quick recovery. It is widely used for benign breast lesions and is playing an increasingly important role in the therapeutic management of non-surgical patients or patients who refuse surgery. Optimal outcomes require an understanding of device features to tailor treatment to each lesion. The Mammotome^® Elite 10G operates in a fixed mode, while the Mammotome^® Revolve EX 8G offers multiple aspiration levels and aperture windows for greater versatility. This study analyzed the specimen features (weight and length), comparing the weight obtained from two different VAE systems to aid the appropriate selection of a device based on the clinical setting. It also determined the number of specimens needed to achieve the 4 g diagnostic threshold. Methods: The Mammotome^® Elite 10G and the Mammotome^® Revolve EX were evaluated under controlled conditions. For Mammotome^® Revolve EX, combinations of five aspiration levels and three aperture lengths (12 mm, 18 mm, and 25 mm) were tested. Twelve samples were collected from a chicken breast phantom for each setting. Specimen weights and the minimum excisions required to reach the 4 g threshold were analyzed. Results: The mean weight per sample for the Mammotome^® Elite 10G was 0.16 ± 0.04 g. For the Mammotome^® Revolve EX, the weights increased with aperture size and aspiration level, ranging from a minimum of 0.132 ± 0.028 g (a window length of 12 mm and aspiration level 1) to a maximum of 0.407 ± 0.055 g (a window length of 25 mm and aspiration level 5). The 25 mm window at aspiration level 5 achieved the 4 g threshold in as few as 10 samples. By comparison, the Mammotome^® Elite required up to 26 samples. Conclusions: Compared to the Mammotome Elite, Mammotome^® Revolve EX offers superior versatility and efficiency, reducing patient discomfort by minimizing the required samples. Its technical advantages make it a valuable tool for both diagnostic and therapeutic applications.