Human epidermal growth factor receptor 2 (HER2) is an important biomarker for predicting prognosis and effectiveness of HER2-targeted therapy in breast cancer. The emergence of novel HER2 antibody-drug conjugate has led to a shift from the binary categorization of HER2 status (i.e., negative and positive) to a ternary categorization gradually ( i.e., HER2 0, HER2 low expression and HER2 positive). The heterogeneity of HER2 low expression in breast cancer has also recently aroused widespread concern, and the heterogeneity of tumors has led to differences in the efficacy of HER2-targeted therapy. Therefore, it is crucial to accurately identify the HER2 expression status of breast cancer, which can provide a basis for patients to formulate personalized treatment strategies. In recent years, artificial intelligence (AI) has developed rapidly and been widely used in the pathological accurate diagnosis of breast cancer. The research results show that AI can significantly improve the consistency and accuracy of HER2 interpreted by pathologists in breast cancer. This has provoked many discussions on HER2-low breast cancer, such as quality control prior to HER2-low expression detection, the latest progress of tumor heterogeneity, and the application of AI. In this paper, we discuss the latest testing guidelines and advances on HER2-low breast cancer, aiming to standardize and improve the pathological testing of HER2-low breast cancer.