Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects

Omneya Attallah

doi:10.62762/JCIB.2025.683401

Article Information

Published in Journal of Computational Intelligence in Biomedicine

Volume/Issue Volume 1, Issue 1, 2026

Pages 10-23

Abstract

Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches utilized for classification, segmentation, and detection, emphasize notable findings, and describe large language models for breast cancer diagnosis. Additionally, the editorial will rigorously examine the existing limitations, significant obstacles, and ethical implications related to the extensive implementation of AI in clinical practice. Ultimately, it will focus on the future, delineating emerging patterns and the progression of AI in influencing the next generation of precision medicine for breast cancer patients.

Keywords

breast cancer artificial intelligence deep learning large language models computer aided diagnosis segmentation medical imaging

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

The author declares no conflicts of interest.

AI Use Statement

Generative artificial intelligence (AI) tools were utilized to assist in the preparation and drafting of this manuscript. All AI-generated content was carefully reviewed, critically revised, and validated by the author. The author assumes full responsibility for the accuracy, integrity, and originality of the final manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Attallah, O. (2026). Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects. Journal of Computational Intelligence in Biomedicine, 1(1), 10–23. https://doi.org/10.62762/JCIB.2025.683401

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TY  - JOUR
AU  - Attallah, Omneya
PY  - 2026
DA  - 2026/04/16
TI  - Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects
JO  - Journal of Computational Intelligence in Biomedicine
T2  - Journal of Computational Intelligence in Biomedicine
JF  - Journal of Computational Intelligence in Biomedicine
VL  - 1
IS  - 1
SP  - 10
EP  - 23
DO  - 10.62762/JCIB.2025.683401
UR  - https://www.icck.org/article/abs/JCIB.2025.683401
KW  - breast cancer
KW  - artificial intelligence
KW  - deep learning
KW  - large language models
KW  - computer aided diagnosis
KW  - segmentation
KW  - medical imaging
AB  - Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches utilized for classification, segmentation, and detection, emphasize notable findings, and describe large language models for breast cancer diagnosis. Additionally, the editorial will rigorously examine the existing limitations, significant obstacles, and ethical implications related to the extensive implementation of AI in clinical practice. Ultimately, it will focus on the future, delineating emerging patterns and the progression of AI in influencing the next generation of precision medicine for breast cancer patients.
SN  - request pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Attallah2026Artificial,
  author = {Omneya Attallah},
  title = {Artificial Intelligence in Breast Cancer Diagnosis: Current Trends, Limitations, and Future Prospects},
  journal = {Journal of Computational Intelligence in Biomedicine},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {10-23},
  doi = {10.62762/JCIB.2025.683401},
  url = {https://www.icck.org/article/abs/JCIB.2025.683401},
  abstract = {Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches utilized for classification, segmentation, and detection, emphasize notable findings, and describe large language models for breast cancer diagnosis. Additionally, the editorial will rigorously examine the existing limitations, significant obstacles, and ethical implications related to the extensive implementation of AI in clinical practice. Ultimately, it will focus on the future, delineating emerging patterns and the progression of AI in influencing the next generation of precision medicine for breast cancer patients.},
  keywords = {breast cancer, artificial intelligence, deep learning, large language models, computer aided diagnosis, segmentation, medical imaging},
  issn = {request pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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