Current applications and future challenges of machine learning and artificial intelligence in clinical trials: A scoping review

Background: Machine learning (ML) and artificial intelligence (AI) applications have increased across different stages of clinical research. Their use in clinical trials (CTs) has been discussed but not quantified. Methods: A scoping review was conducted by searching PubMed, Embase (Ovid), and Scopus for CTs or protocols. The goal was to understand the extent of ML and AI applications in the design, conduct, and analysis of CTs. Screening was performed on Covidence, with GPT model support. Findings: After title/abstract and full-text screening, 108 records were included; in some studies, AI/ML was applied across multiple stages. For the design, 20 studies involved advanced methods, six applied them to stratification, four to treatment selection during randomization, six to participant selection, two for outcome assessment, and two for site selection. Seven studies involved them in the collection and analysis of data from wearable devices, and one for monitoring. More commonly, AI/ML has been used at the analysis stage of 93 CTs; however, limitations in reporting trial objectives make it difficult to distinguish the purpose between primary and exploratory analyses. Interpretation: This research identifies a serious mismatch between the potential and actual applications of ML in CTs. Considering the potential benefits of ML in CTs, such underuse could hinder the evolution of CTs toward faster and more efficient approaches.