Large language models in clinical trials: applications, technical advances, and future directions.

Abstract

Background: As clinical trials scale up and grow more complex, researchers are facing mounting challenges, including inefficient participant recruitment, complex data management, and limited risk monitoring. These issues not only increase the workload for clinical researchers but also compromise trial reliability and safety, potentially elevating the risk of trial failure. Large language models (LLMs), as an emerging technology in natural language processing (NLP), exhibit notable advantages across various tasks, such as information extraction and relation classification.

Main text: With domain-specific pre-training and fine-tuning, LLMs present promising potential in clinical trial tasks such as automated patient-trial matching and the extraction and processing of trial data, which are anticipated to reduce time and financial costs. Additionally, they offer valuable insights for scientific rationale, medical decision-making, and trial endpoint prediction. In this context, an increasing number of studies have begun to explore the applications of LLMs in the design and conduct of clinical trials.

Conclusion: This paper provides a review of LLM applications in clinical trials with an emphasis on real-world integration. Comparative advantages over traditional NLP models, technical limitations, and future implementation challenges are also discussed. This narrative review aims to highlight the potential of LLMs in clinical trial workflows and clarify key challenges and future directions.