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The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine

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arxiv 2505.10261 v1 pith:RRN7KBOF submitted 2025-05-15 cs.CL cs.AI

Rui Yang , Huitao Li , Matthew Yu Heng Wong , Yuhe Ke , Xin Li , Kunyu Yu , Jingchi Liao , Jonathan Chong Kai Liew
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Sabarinath Vinod Nair Jasmine Chiat Ling Ong Irene Li Douglas Teodoro Chuan Hong Daniel Shu Wei Ting Nan Liu
This is my paper · ORCID
classification cs.CLcs.AI
keywords languagegenerativetaskslargellmsmedicalmedicinemodels
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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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