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

Area of Science:

• Clinical Research Informatics
• Artificial Intelligence in Medicine
• Natural Language Processing

Background:

• Clinical trials face increasing complexity, leading to challenges in participant recruitment, data management, and risk monitoring.
• These issues can compromise trial reliability, safety, and increase the risk of failure.
• Large language models (LLMs) show promise in addressing these challenges through advanced natural language processing (NLP) capabilities.

Purpose of the Study:

• To review the applications of LLMs in clinical trial design and conduct.
• To emphasize the real-world integration of LLMs in clinical trial workflows.
• To discuss the comparative advantages, technical limitations, and future challenges of LLM implementation.

Main Methods:

• Domain-specific pre-training and fine-tuning of LLMs for clinical trial tasks.
• Exploration of LLM applications in automated patient-trial matching.
• Analysis of LLM capabilities in data extraction, processing, and insight generation for clinical trials.

Main Results:

• LLMs demonstrate potential in automating patient-trial matching, reducing time and costs.
• LLMs can extract and process clinical trial data, offering insights for scientific rationale and decision-making.
• Studies increasingly explore LLM integration into clinical trial design and execution.

Conclusions:

• LLMs present significant potential to enhance clinical trial workflows by improving efficiency and reliability.
• Comparative advantages over traditional NLP models are evident, alongside existing technical limitations.
• Addressing implementation challenges is crucial for the future success of LLMs in clinical research.