The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine
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Abstract (arXiv)
Natural language processing (NLP) has been traditionally applied to medicine, and generative large language models (LLMs) have become prominent recently. However, the differences between them across different medical tasks remain underexplored. We analyzed 19,123 studies, finding that generative LLMs demonstrate advantages in open-ended tasks, while traditional NLP dominates in information extraction and analysis tasks. As these technologies advance, ethical use of them is essential to ensure their potential in medical applications.
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References · 6
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
- 2024KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniquescited by 37
- 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
- 2025Generative Large Language Models Trained for Detecting Errors in Radiology Reportscited by 5
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