Artificial Intelligence and Breast Cancer Management: From Data to the Clinic

Abstract

Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC-related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient-oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.

Keywords: artificial intelligence; breast cancer; clinical practice; deep learning; machine learning.

Figure 1

Overview of the whole‐cycle involvement of AI in BC management. The application of AI has become prevalent in BC diagnosis, prognosis, and treatment methods. By processing and analyzing clinical information, medical images, and other patient‐related data, AI aids healthcare professionals in implementing personalized health management for patients. Furthermore, AI encounters numerous challenges in its clinical applications. AE, autoencoder; AI, artificial intelligence; BC, breast cancer; CNN, convolutional neural network; DRL, deep reinforcement learning; GAN, graph adversarial network; GNN, graph neural network; NN, neural network; RNN, recurrent neural network.

Figure 2

Number of published articles related to the application of AI in cancer and BC, as well as BC diagnosis and prognosis, over the past decade. The PubMed database was searched using the terms “cancer,” “breast cancer,” “diagnosis,” “prognosis,” “deep learning,” and “machine learning.” AI, artificial intelligence; BC, breast cancer.

Figure 3

AI‐powered anticancer drug discovery. AI applications in the drug discovery pipeline, including target identification, clinical trial development, and registration, offer notable advantages, such as enhanced efficiency and cost savings. ADME/T, absorption, distribution, metabolism, excretion, and toxicity; AI, artificial intelligence.