Page 106 - AIH-2-2

Artificial Intelligence in Health

ORIGINAL RESEARCH ARTICLE
Artificial intelligence for early diagnosis of breast

cancer in women: A systematic literature review

Saadia Humayun and Tariq Mahmood *
Department of Mathematics and Computer Science, Institute of Business Administration,
University of Karachi, Karachi, Sindh, Pakistan

Abstract
Breast cancer is one of the most prevalent cancers affecting women globally. Early
diagnosis is crucial for effective treatment and improved survival rates. Imaging
techniques such as mammography and ultrasound are widely used conventional
diagnostic methods. However, these methods have limitations, including low
sensitivity and specificity, especially in patients with dense breast tissue. For
instance, mammograms miss approximately 20% of breast cancer cases, leading
to false negatives and delayed treatment that can have fatal consequences. To
address these challenges, artificial intelligence (AI)-based diagnostic tools have
been developed to assist healthcare professionals in accurately detecting breast
cancer. These tools work in conjunction with human radiologists to improve
*Corresponding author: diagnostic outcomes. In addition, biomarkers present a promising non-invasive,
Tariq Mahmood more convenient alternative for the early detection of breast cancer, potentially
(tmahmood@iba.edu.pk) overcoming the limitations of traditional screening methods. Various biomarkers,
Citation: Humayun S, Mahmood T. such as circulating tumor cells, cell-free tumor nucleic acids, and microRNAs, have
Artificial intelligence for early shown promise in early breast cancer diagnosis. A systematic literature review
diagnosis of breast cancer in
women: A systematic literature is needed to consolidate ongoing efforts in molecular biology and biomedical
review. Artif Intell Health. sciences aimed at achieving early breast cancer diagnosis. One of the limitations
2025;2(2):100-116. of previously published research is the heterogeneity of methodologies, which
doi: 10.36922/aih.4197
can compromise the credibility of comparisons due to potential inaccuracies in
Received: July 10, 2024 the original data. Hence, future studies should prioritize using consistent datasets
1st revised: October 31, 2024 and developing robust techniques to manage missing values, outliers, and class
imbalances to improve the reliability of breast cancer detection models. This
2nd revised: November 21, 2024
literature review seeks to bridge the knowledge gap by reporting recent high-
Accepted: December 20, 2024 performing AI models and effective biomarkers that can serve as diagnostic tools
Published online: January 13, in clinical practice.
2025
Copyright: © 2025 Author(s). Keywords: Early diagnosis; Breast cancer; Computer-aided diagnosis; Artificial
This is an Open-Access article
distributed under the terms of the intelligence in healthcare; Deep learning; Cancer biomarkers
Creative Commons Attribution
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited. 1. Introduction
Publisher’s Note: AccScience Breast cancer remains a significant global health concern, ranking as the second most
Publishing remains neutral with prevalent cancer worldwide and the primary cause of cancer-related mortality among
regard to jurisdictional claims in
published maps and institutional women. In 2020, approximately 2,296,840 new cases were diagnosed globally, with an
affiliations. estimated 670,000 women succumbing to the disease. 1,2

Volume 2 Issue 2 (2025) 100 doi: 10.36922/aih.4197