# Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis

## TL;DR

This paper reviews how artificial intelligence is being used to improve diagnosis, treatment response prediction, and prognosis in triple-negative breast cancer.

## Contribution

The paper provides a comprehensive review of recent AI applications specifically for triple-negative breast cancer, emphasizing clinical translation challenges and opportunities.

## Key findings

- AI helps address diagnostic challenges in triple-negative breast cancer due to variable imaging features.
- AI can improve treatment response prediction despite heterogeneous patient responses.
- AI offers potential for better prognosis by overcoming limitations of traditional clinicopathologic factors.

## Abstract

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with limited targeted treatment options, heterogeneous treatment response, and high risk of early recurrence. Artificial intelligence (AI) has rapidly emerged as a powerful tool to address key clinical challenges in TNBC across diagnosis, treatment response assessment, and prognosis. Diagnostic and staging challenges persist due to variable imaging features in TNBC and limitations in conventional modalities, increasing the risk of delayed detection. Predicting response to neoadjuvant systemic therapy remains difficult, as patient responses are heterogeneous, and existing clinical markers provide limited early predictive value. Prognostication in TNBC is similarly constrained by the absence of widely used genomic tools and reliance on clinicopathologic factors that incompletely reflect tumor biology. This review summarizes recent advances in AI applications for TNBC across diagnosis, tumor characterization and staging, treatment response prediction, and prognosis, highlighting both emerging opportunities and current limitations in clinical translation.