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Summary

Artificial intelligence (AI) is revolutionizing breast cancer screening through risk prediction models. This review explores AI advancements for predicting breast cancer likelihood, aiming for improved clinical adoption and patient outcomes.

Keywords:
Artificial intelligenceBreast cancer screeningMammographyRisk prediction

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) presents two main applications in breast cancer screening: computer-aided detection (CAD) and risk prediction.
  • While AI CAD systems are gradually entering clinical use, AI risk models are primarily in research settings.
  • AI risk models aim to forecast a patient's probability of developing breast cancer post-screening.

Purpose of the Study:

  • To synthesize recent advancements in AI-driven breast cancer risk prediction models.
  • To critically evaluate methodologies ranging from traditional imaging biomarkers to deep learning and multimodal approaches.
  • To discuss challenges and propose future directions for AI model implementation in clinical practice.

Main Methods:

  • Review of current literature on AI applications in breast cancer risk prediction.
  • Analysis of traditional imaging biomarkers, deep learning techniques, and multimodal data integration.
  • Critical appraisal of research contributions, focusing on methods and findings.

Main Results:

  • AI risk models show promise in predicting breast cancer likelihood, utilizing diverse data and advanced methodologies.
  • Significant progress has been made in developing sophisticated AI algorithms for risk assessment.
  • Challenges related to clinical adoption, ethics, and real-world application persist.

Conclusions:

  • AI risk models offer a powerful tool for enhancing breast cancer screening strategies.
  • Further research and development are needed to overcome implementation barriers and ensure equitable access.
  • Optimizing AI adoption can lead to improved patient outcomes and more personalized screening approaches.