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Artificial intelligence for breast cancer management.

Bryan Nicholas Chua^1,2, Dexter Kai Hao Thng², Tan Boon Toh^3,4,5

¹The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Communications Medicine

|January 4, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing breast cancer care by improving detection, diagnosis, and treatment. While AI offers significant potential for personalized medicine and better patient outcomes, rigorous validation and equitable implementation are crucial for clinical adoption.

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Area of Science:

Oncology
Medical Informatics
Biotechnology

Background:

Breast cancer management is complex, involving early detection, accurate diagnosis, prognosis, and tailored treatment.
Advancements in artificial intelligence (AI) offer new tools for analyzing diverse patient data.
AI has the potential to enhance precision medicine approaches in oncology.

Purpose of the Study:

To review AI-driven advancements in breast cancer management.
To evaluate the efficacy, limitations, and clinical impact of AI applications.
To identify challenges hindering the translation of AI into clinical practice.

Main Methods:

Comprehensive literature review of AI applications in breast cancer.
Analysis of AI's role in analyzing medical imaging, histopathology, genomics, and multi-omics data.
Evaluation of AI's impact on detection, diagnosis, prognosis, treatment, and recovery.

Main Results:

AI demonstrates improved accuracy in breast cancer detection and molecular subtyping.
AI enhances prognostic accuracy and facilitates personalized therapeutic strategies.
AI applications show potential for improving quality of life interventions for patients.

Conclusions:

AI is transforming breast cancer management, offering enhanced precision and patient-tailored strategies.
Key challenges include generalisability, reproducibility, regulatory barriers, and the need for rigorous validation.
Equitable implementation and transparent model development are essential for realizing AI's full clinical potential.