Clinical Trials Ontology Engineering with Large Language Models

TL;DR

This paper demonstrates that large language models like GPT-3.5, GPT-4, and Llama3 can effectively automate the extraction and integration of clinical trial data, reducing time and costs while maintaining quality.

Contribution

It introduces a methodology leveraging LLMs for clinical trial ontology engineering, showing their viability compared to human efforts.

Findings

LLMs significantly reduce time and cost in ontology creation.

LLMs produce comparable quality to human-created ontologies.

Real-time data integration in medical research becomes feasible.

Abstract

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.