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<p>We analyzed 19,123 natural language processing-related studies to explore the differences in task distributions and application contexts between large language models (LLMs) and non-LLM methods in health care. Through topic modeling analysis, we found that LLMs demonstrate advantages in open-ended tasks, while non-LLM methods dominate in information extraction tasks. These findings highlight the complementary strengths of the two technical paradigms and provide reference for their integration strategies in future health care applications.</p>

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The evolving landscape of large language models and non-large language models in health care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月9日

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  • Rui Yang,
  • Huitao Li,
  • Matthew Yu Heng Wong,
  • Yuhe Ke,
  • Xin Li,
  • Kunyu Yu,
  • Jingchi Liao,
  • Jonathan Chong Kai Liew,
  • Sabarinath Vinod Nair,
  • Jasmine Chiat Ling Ong,
  • Irene Li,
  • Douglas Teodoro,
  • Chuan Hong,
  • Yifan Peng,
  • Daniel Shu Wei Ting,
  • Nan Liu

摘要

We analyzed 19,123 natural language processing-related studies to explore the differences in task distributions and application contexts between large language models (LLMs) and non-LLM methods in health care. Through topic modeling analysis, we found that LLMs demonstrate advantages in open-ended tasks, while non-LLM methods dominate in information extraction tasks. These findings highlight the complementary strengths of the two technical paradigms and provide reference for their integration strategies in future health care applications.