Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development, promoting interdisciplinary integration. Recent advancements in artificial intelligence (AI) technologies have brought unprecedented opportunities to this field. The integration of various AI models allows researchers to better utilize multi-omics data, identify disease phenotypes, interpret animal behavior, assess treatment effects, improve experimental designs, reduce the use of experimental animals, enhance animal facility management, and improve animal welfare. This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification, the selection and design of experimental animal models, animal behavior analysis, and animal facility management. It also points out the challenges related to data standardization, AI model selection and interpretability, the extrapolation process from AI models to animal experiments and clinical practice, as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine. This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development, thus providing support for its broader application.