In this review, papers under three main topics related to clinical trial outcome prediction, clinical text embedding, complex trial relations, and trial prediction methods are systematically reviewed. These studies demonstrate that AI has the potential to extract features from multimodal biomedical data and make valid predictions. However, there is still much room for improvement, especially in terms of representing biomedical data more comprehensively, processing missing or imbalanced data, and enhancing the generalizability of AI models.