The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine
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arXiv
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| Format: | Preprint |
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2025
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| author | Yang, Rui Li, Huitao Wong, Matthew Yu Heng Ke, Yuhe Li, Xin Yu, Kunyu Liao, Jingchi Liew, Jonathan Chong Kai Nair, Sabarinath Vinod Ong, Jasmine Chiat Ling Li, Irene Teodoro, Douglas Hong, Chuan Ting, Daniel Shu Wei Liu, Nan |
| author_facet | Yang, Rui Li, Huitao Wong, Matthew Yu Heng Ke, Yuhe Li, Xin Yu, Kunyu Liao, Jingchi Liew, Jonathan Chong Kai Nair, Sabarinath Vinod Ong, Jasmine Chiat Ling Li, Irene Teodoro, Douglas Hong, Chuan Ting, Daniel Shu Wei Liu, Nan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10261 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine Yang, Rui Li, Huitao Wong, Matthew Yu Heng Ke, Yuhe Li, Xin Yu, Kunyu Liao, Jingchi Liew, Jonathan Chong Kai Nair, Sabarinath Vinod Ong, Jasmine Chiat Ling Li, Irene Teodoro, Douglas Hong, Chuan Ting, Daniel Shu Wei Liu, Nan Computation and Language Artificial Intelligence 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. |
| title | The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10261 |