Table 1. Summary of potential applications of large language models on clinical trials^*.

Area of application	Details	Related Examples
Enhance patient-trial matching	Automate pre-screening using LLMs, streamline evaluation of eligibility criteria, and produce step-by-step reasoning of output.	- Cross-referencing medical profiles with eligibility criteria [9].
		- Predicting trial-level eligibility scores [10].
Streamline clinical trial planning	Process extensive text data, generate coherent text from simple descriptions, and predict clinical trial outcomes.	- Summarizing clinical trial data [12].
		- Creating criterion descriptions [13].
		- Predicting trial outcomes [14].
Applications on free text narratives	Enhance the consistency and accuracy of data coding from free text.	- Classifying electronic health records [17].
		- Coding text data requiring deductive analysis [19].
Assistance in technical writing	Automate medical document writing and convert between tabular data and free-form text.	- Generation of patient discharge summaries [22].
		- Summarization of radiology reports [23].
Provide cognizant consent	Improve comprehension of consent through LLM-powered chatbots and generate text for knowledge gaps.	- LLMs providing answers based on the most recent information [27].
		- Assessing knowledge and filling gaps [29].

LLM, large language model.

^*The manuscript, excluding the introduction and conclusions sections, was input into ChatGPT-4.0, and then prompted to create a summary table. For the detailed prompt, refer to the following link: https://chat.openai.com/share/537912e5-fdb0-481c-aeb2-da1eb29f77da