Abstract: The diagnosis of pneumoconiosis is mainly based on medical imaging. Artificial intelligence (AI) aided diagnosis of pneumoconiosis has been applied in medical imaging nowadays. It is not difficult to diagnose pneumoconiosis in the second and third stages, but it is difficult to diagnose non-pneumoconiosis and stage-one pneumoconiosis, and the results are often discrepant, especially for physicians with limited experience who would probably fail to diagnose or misdiagnose. The application of AI technology in image diagnosis of pneumoconiosis is to solve the problem of classification of non-pneumoconiosis and stage-one pneumoconiosis. This paper summarized the application of AI technology to pneumoconiosis diagnosis in recent years, focusing on support vector machines (SVM) and artificial neural network (ANN) applied to the classification of pneumoconiosis, and on their advantages and disadvantages. The possibility of applying convolutional neural network (CNN) technology and other AI deep learning algorithms to pneumoconiosis image classification in the future was prospected by analyzing existing difficulties and possible directions of future breakthrough.