Opportunities and Pitfalls with Large Language Models for Biomedical Annotation

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Author(s)Arighi, Cecilia
Author(s)Kim, Jin-Dong
Author(s)Lu, Zhiyong
Author(s)Rinaldi, Fabio
Date Accessioned2024-12-16T20:31:40Z
Date Available2024-12-16T20:31:40Z
Publication Date2024-12
DescriptionThis article was originally published in Biocomputing 2025. The version of record is available at: https://doi.org/10.1142/9789819807024_0052. © 2024 The Authors. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/).
AbstractLarge language models (LLMs) and biomedical annotations have a symbiotic relationship. LLMs rely on high-quality annotations for training and/or fine-tuning for specific biomedical tasks. These annotations are traditionally generated through expensive and time-consuming human curation. Meanwhile LLMs can also be used to accelerate the process of curation, thus simplifying the process, and potentially creating a virtuous feedback loop. However, their use also introduces new limitations and risks, which are as important to consider as the opportunities they offer. In this workshop, we will review the process that has led to the current rise of LLMs in several fields, and in particular in biomedicine, and discuss specifically the opportunities and pitfalls when they are applied to biomedical annotation and curation.
CitationArighi, Cecilia, Jin-Dong Kim, Zhiyong Lu, and Fabio Rinaldi. “Opportunities and Pitfalls with Large Language Models for Biomedical Annotation.” In Biocomputing 2025, 706–10. Kohala Coast, Hawaii, USA: WORLD SCIENTIFIC, 2024. https://doi.org/10.1142/9789819807024_0052.
ISBN9789819807017
URLhttps://udspace.udel.edu/handle/19716/35666
Languageen_US
PublisherBiocomputing 2025
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
Keywordslarge language model
KeywordsLLM
Keywordsbiomedical curation
Keywordsgenerative AI
Keywordsbiomedicine and health
Keywordseducation
Keywordsethics
TitleOpportunities and Pitfalls with Large Language Models for Biomedical Annotation
TypeArticle
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