Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review
Long Qian, Xin Lu, Parvez I. Haris, Jianyong Zhu, Shuo Li, Yingjie Yang
Abstract
• Clinical trials are vital for drug development but demand substantial time and financial resources. • Uncertainties in clinical trial results can stem from drug effectiveness, safety, or participant enrollment issues. • Robust AI models forecasting clinical trial results can prevent failures and accelerate drug discovery. • This article reviews research on three AI methodologies: clinical text embedding, trial relations, and outcome prediction. • Real - world applications of predicting clinical trial outcomes present challenges and opportunities. 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.