Transforming breast cancer care: the critical role of digital pathology and artificial intelligence in biomarker testing and risk stratification

Comprehensive Summary

Currently, breast cancer diagnostic assessments, including biomarker testing such as estrogen receptor (ER), progesterone receptor (PR), and HER2 evaluation, are subject to significant inter-observer variability, highlighting the need for more standardized and reproducible diagnostic strategies. This review suggests a combination of digital pathology and artificial intelligence, in which AI based systems analyze quantitative features from digital data. The data shows that deep learning models trained on annotated histopathology images can accurately quantify ER and PR and analyze HER2 staining patterns. Overall, this review shows how AI can eliminate biases and observer fatigue when diagnosing breast cancer.

Outcomes and Implications

This article demonstrates that AI-assisted digital pathology facilitates standardized and reproducible diagnostic strategies, leading to improved accuracy in breast cancer diagnosis.