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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2412.14387 |
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| _version_ | 1866913618374688768 |
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| author | Çakır, Berkan |
| author_facet | Çakır, Berkan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clinical Trials Ontology Engineering with Large Language Models Çakır, Berkan Artificial Intelligence 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. |
| title | Clinical Trials Ontology Engineering with Large Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.14387 |