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
Pesapane et al. (2025) studied this question.