Artificial Intelligence-Based Breast Cancer Diagnosis

Abstract

Breast cancer(BC) has emerged as one of the most prevalent malignant affecting worldwide. According to statistic data provided by World Health Organization(WHO), in 2020, the incidence of BC accounts for 11.7% of all cancers. Thus, early stage-prediction and precise late-stage diagnosis play a crucial role in treatment process. From 2020, the technology of artificial intelligence(AI) has been gradually matured, enabling BC prediction and diagnose through image-analysis, pathological assistance and risk-assessment using deep learning CNN, WSI, and combining with genome datasets. Consequently, prediction type models like Orpheus, CSCO AI and diagnostic models like BMU-Net and Lunit Insight MMG all assist in treating BC. The value of AI is also vital in decision-making by integrating multi-omics data, recommending personalized medicine, and suggesting later healthy prognosis. Notable progress has been achieved in this area, for instance, AI enhances early screening by identifying omitted BC and promoting diagnosis accuracy in early screening; it also optimizes personalized treatment plans by adjusting latest circumstances and assisting complicated clinical decision-making. However, AI still requires a large quantity of training datasets to fix their drawbacks on data standardization, ethical compliance and clinical implementation. In the future, AI is expected further to combine multi-omics data with deep learning to better foster the treatment in BC.