Clinical Trials Ontology Engineering with Large Language Models
This addresses a problem for the medical industry by enabling more efficient and cost-effective clinical trial data management, though it appears incremental as it applies existing LLMs to a specific domain.
The paper tackled the challenge of managing clinical trial information by proposing a method using large language models to extract and integrate data, finding that models like GPT-3.5, GPT-4, and Llama3 are viable for automation, reducing time and cost compared to human efforts.
Managing clinical trial information is currently a significant challenge for the medical industry, as traditional methods are both time-consuming and costly. This paper proposes a simple yet effective methodology to extract and integrate clinical trial data in a cost-effective and time-efficient manner. Allowing the medical industry to stay up-to-date with medical developments. Comparing time, cost, and quality of the ontologies created by humans, GPT3.5, GPT4, and Llama3 (8b & 70b). Findings suggest that large language models (LLM) are a viable option to automate this process both from a cost and time perspective. This study underscores significant implications for medical research where real-time data integration from clinical trials could become the norm.