A scoping review of artificial intelligence applications in clinical trial risk assessment.

ABSTRACT: Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzed 142 studies published between 2013 and 2024, focusing on safety (n = 55), efficacy (n = 46), and operational (n = 45) risk prediction. AI techniques, including traditional machine learning, deep learning (e.g., graph neural networks, transformers), and causal machine learning, are used for tasks like adverse drug event prediction, treatment effect estimation, and phase transition prediction. These methods utilize diverse data sources, from molecular structures and clinical trial protocols to patient data and scientific publications. Recently, large language models (LLMs) have seen a surge in applications, featuring in 7 out of 33 stud

SUBMITTER: Teodoro D 

PROVIDER: S-EPMC12307910 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature