Dry eye(DE)is one of the most common eye diseases worldwide, with prevalence ranging from 5% to 50%. DE cannot be diagnosed timely and accurately due to its complex etiology and the limitations of testing equipment. In recent years, with the widespread use of artificial intelligence(AI)in the medical field, the application of machine learning and deep learning in the detection of dry eye has been deeply studied, such as interferometry, slit lamp examination and the classification and evaluation of meibomian gland images. Studies have found that the AI models can accurately analyze the measured data and images of patients with dry eye and with sensitivity and specificity of more than 90%. AI has great potential to assist clinicians in the objective diagnosis of dry eye and improve the quality of life of patients with dry eye. In this review, we summarized the current status of AI in dry eye, the potential challenges in clinical application, and look forward to the prospect of AI-assisted diagnosis of dry eye.