Artificial intelligence (AI) refers to a computer imitating “intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.”[1] Machine learning (ML) is a core area of AI to create predictive models by learning from data and gradually enhancing the capacity for prediction through experience.[2] The integration of AI and ML into animal research has shown promising potential to enhance translation and reproducibility, complementing traditional approaches such as animal models. AI and ML can optimize preclinical studies using animal models by analyzing complex datasets, improving experimental design, and predicting outcomes. This integration enables researchers to extract more meaningful information from animal experiments.[3] Combining AI/ML analyses of animal model data with human clinical data allows for better translation of findings. This integrated approach helps bridge the gap between preclinical and clinical studies, increasing the relevance of animal model findings to human disease. A combination of transcriptomic analysis (studying gene expression patterns) in postmortem human brain tissue from Alzheimer’s disease patients and mouse models of Alzheimer’s disease was done with the help of ML to identify dysregulated pathways associated with excitatory neurotransmission, a process crucial for brain function.[4]