Using Large Language Models to Generate Clinical Trial Tables and Figures
This addresses a specific problem for clinical trial researchers by automating TFL generation, though it appears incremental as it applies existing LLM methods to a new domain.
The study tackled the time-consuming task of creating tables, figures, and listings (TFLs) for clinical trials by using large language models (LLMs) to automate their generation, demonstrating efficient results with prompt engineering and few-shot learning on public data.
Tables, figures, and listings (TFLs) are essential tools for summarizing clinical trial data. Creation of TFLs for reporting activities is often a time-consuming task encountered routinely during the execution of clinical trials. This study explored the use of large language models (LLMs) to automate the generation of TFLs through prompt engineering and few-shot transfer learning. Using public clinical trial data in ADaM format, our results demonstrated that LLMs can efficiently generate TFLs with prompt instructions, showcasing their potential in this domain. Furthermore, we developed a conservational agent named Clinical Trial TFL Generation Agent: An app that matches user queries to predefined prompts that produce customized programs to generate specific predefined TFLs.