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<p>Artificial intelligence is transforming breast cancer management through various machine learning applications. Artificial intelligence supports precision medicine by enhancing detection, diagnosis, prognosis, and treatment response prediction. It achieves this by analysing data from medical imaging, histopathology, genomics and multi-omics sources to improve patient recovery. This review summarises AI-driven advancements across the entire continuum of breast cancer management, spanning detection, diagnosis, prognosis, treatment and recovery. It evaluates their efficacy and limitations, explores their impact on healthcare costs and clinical practice, and addresses key challenges including generalisability, reproducibility and regulatory barriers. Evidence from recent studies highlights AI’s role in improving breast cancer detection, molecular subtyping and prognostic accuracy. It also facilitates more patient-tailored therapeutic strategies and supports quality of life interventions. Nonetheless, the translation of these benefits into clinical practice requires rigorous validation, transparent model development, and equitable implementation.</p><p></p>

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Artificial intelligence for breast cancer management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月3日

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  • Bryan Nicholas Chua,
  • Dexter Kai Hao Thng,
  • Tan Boon Toh,
  • Dean Ho

摘要

Artificial intelligence is transforming breast cancer management through various machine learning applications. Artificial intelligence supports precision medicine by enhancing detection, diagnosis, prognosis, and treatment response prediction. It achieves this by analysing data from medical imaging, histopathology, genomics and multi-omics sources to improve patient recovery. This review summarises AI-driven advancements across the entire continuum of breast cancer management, spanning detection, diagnosis, prognosis, treatment and recovery. It evaluates their efficacy and limitations, explores their impact on healthcare costs and clinical practice, and addresses key challenges including generalisability, reproducibility and regulatory barriers. Evidence from recent studies highlights AI’s role in improving breast cancer detection, molecular subtyping and prognostic accuracy. It also facilitates more patient-tailored therapeutic strategies and supports quality of life interventions. Nonetheless, the translation of these benefits into clinical practice requires rigorous validation, transparent model development, and equitable implementation.