Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized...

Area of Science:

• Oncology
• Medical Imaging
• Artificial Intelligence

Background:

• Current age-based breast cancer screening has limitations, including overdiagnosis and missed cancers.
• Risk-stratified screening approaches are gaining traction to improve accuracy and reduce harms.
• Artificial intelligence (AI) is a key technology driving advancements in personalized screening.

Purpose of the Study:

• To review the role of AI in advancing breast cancer screening toward personalized approaches.
• To examine AI-driven mammographic risk models, multimodal risk prediction, and clinical decision support tools.
• To assess the current evidence, challenges, and future directions for AI in breast cancer screening.

Main Methods:

• Narrative review of studies published from 2015 to 2025.
• Prioritization of large cohorts, randomized trials, and prospective validations.
• Focus on AI applications in mammographic risk modeling, multimodal data integration, and clinical decision support.

Main Results:

• AI-based mammographic risk models show improved discrimination compared to traditional models, with external validation ongoing.
• Evidence for AI models is heterogeneous across breast cancer subtypes, with stronger signals for ER-positive disease.
• Multimodal models integrating diverse data types and AI for triage/personalized intervals are emerging.
• Barriers include explainability, regulatory approval, and ensuring equitable access.

Conclusions:

• AI tools show promise for personalizing breast cancer screening, particularly mammographic risk assessment.
• Further prospective trials are needed to confirm clinical outcome benefits and guide personalized screening intervals.
• Careful integration, regulatory alignment, equity monitoring, and clear role definition are crucial for AI adoption in screening workflows.