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Health·G Space·Evidence-backed gain·Published 2026-09-14

Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era

Abstract: Objective Breast cancer (BC) is a worldwide health task, and its increasing frequency and the associated burden on healthcare professionals are also increasing. Hence, integrating machine learning (ML) and artificial intelligence (AI) into screening and diagnosis is irrefutable in the digital age. A comprehensive systematic review was conducted aimed at assessing the application of AI and ML techniques to enhance BC screening, diagnosis, classification, and tumor marker scoring. Methods An electronic literature…

TRV-2026-1079Peer-reviewedPermanent record — cite & verify
Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era

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The quick read

A systematic review published September 12, 2026 reviewed AI and machine learning techniques for breast cancer screening, diagnosis, classification and tumor marker scoring. It examined advanced deep learning models including ANNs, SVMs, CNNs and faster R-CNN applied to cytology, histopathology and combined imaging-pathology data.

The findings matter because breast cancer incidence and burden on healthcare professionals are increasing, and the review points to measurable gains in accuracy, consistency and rapidity that could ease pathology workloads. What remains uncertain from this abstract is real-world performance across diverse populations, regulatory adoption details, and long-term patient outcomes.

Main points
  • Systematic review searched PubMed and Google Scholar for AI, cytology, histology, tumor marker expression, deep learning and machine learning in breast carcinoma pathology.
  • Reviewed models include artificial neural networks, support vector machines, convolutional neural networks, and faster R-CNN applied to cytology and histopathology images.
  • Article notes FDA and internationally approved AI software products for breast cancer detection as evidence of translation potential.
Gain

Systematic review finds AI and deep learning models achieve high accuracy on cytology and histopathology images and offer gains in consistency, speed, cost-effectiveness, and reduced pathologist workload in breast cancer screening and diagnosis.

The rundown

The review was conducted via electronic literature search in PubMed and Google Scholar following systematic review guidelines, including only articles pertaining to breast cancer pathology. It focused on keywords covering AI, cytology, histology, tumor marker expression, deep learning and machine learning.

Results describe AI applications extending to combined imaging-pathology diagnostics and tumor biomarkers, with mention of various Food and Drug Administration and internationally approved AI software products for breast cancer detection illustrating translation potential.

Sources

Reader signal

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The debate