Qian Long, Lu Xin, Haris Parvez, Zhu Jianyong, Li Shuo, Yang Yingjie
Faculty of Computing Engineering Media, De Montfort University, Leicester, UK.
Faculty of Health & Life Sciences, De Montfort University, Leicester, UK.
Drug Discov Today. 2025 Apr;30(4):104332. doi: 10.1016/j.drudis.2025.104332. Epub 2025 Mar 15.
Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review discusses AI methodologies that impact clinical trial outcomes, focusing on clinical text embedding, trial multimodal learning, and prediction techniques, while addressing practical challenges and opportunities.
临床试验在药物研发中起着关键作用，但充满了不确定性且资源需求巨大。应用人工智能模型来预测试验结果可以减少失败并加快药物发现过程。本综述讨论了影响临床试验结果的人工智能方法，重点关注临床文本嵌入、试验多模态学习和预测技术，同时探讨了实际挑战和机遇。
