Background: Breast cancer remains one of the leading malignancies in women with distinct clinical heterogeneity and intense multidisciplinary cooperation. Remarkable progresses have been made in artificial intelligence (AI). A bibliometric analysis was taken to characterize the current picture of development of AI in breast cancer. Materials and Methods: Search process was performed in the Web of Science Core Collection database with analysis and visualization performed by R software, VOSviewer, CiteSpace and Gephi. Latent Dirichlet Allocation (LDA), a machine learning based algorithm, was used for analysis of topic terms. Results: A total of 511 publications in the field of AI in breast cancer were retrieved between 2000 to 2021. A total of 103 publications were from USA with 2482 citations, making USA the leading country in the field of AI in breast cancer, followed by China. Mem Sloan Kettering Canc Ctr, Radboud Univ Nijmegen, Peking Univ, Sichuan Univ, ScreenPoint Med BV, Lund Univ, Duke Univ, Univ Chicago, Harvard Med Sch and Univ Texas MD Anderson Canc Ctr were the leading institutions in the field of AI in breast cancer. AI, breast cancer and classification, mammography were the leading keywords. LDA topic modeling identified top fifty topics relating the AI in breast cancer. A total of five primary clusters were found within the network of fifty topics, including radiology feature, lymph node diagnosis and model, pathological tissue and image, dataset classification and machine learning, gene expression and survival. Conclusions: This research depicted AI studies in breast cancer and presented insightful topic terms with future perspective.