Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review.

Journal: Drug discovery today
PMID: 40097090

Abstract

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.

Authors

  • Long Qian
    Faculty of Computing Engineering Media, De Montfort University, Leicester, UK.
  • Xin Lu
    CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
  • Parvez Haris
    Faculty of Health & Life Sciences, De Montfort University, Leicester, UK.
  • Jianyong Zhu
    Scitops Corporation, Shanghai, China.
  • Shuo Li
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yingjie Yang
    School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.

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