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arXiv · 2412.14387

Clinical Trials Ontology Engineering with Large Language Models

Berkan Çakır
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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.

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Berkan Çakır. 2024-12-18. Clinical Trials Ontology Engineering with Large Language Models. https://arxiv.org/abs/2412.14387

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