Opportunities and Pitfalls with Large Language Models for Biomedical Annotation
Date
2024-12
Journal Title
Journal ISSN
Volume Title
Publisher
Biocomputing 2025
Abstract
Large 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.
Description
This 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/).
Keywords
large language model, LLM, biomedical curation, generative AI, biomedicine and health, education, ethics
Citation
Arighi, 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.