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Large Language Models in Critical Care Medicine: Scoping Review.

Tongyue Shi1,2,3,4, Jun Ma5, Zihan Yu6

  • 1National Institute of Health Data Science, Peking University, Beijing, China.

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|November 24, 2025
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Summary

Large language models (LLMs) show promise in critical care medicine (CCM) for decision support and documentation. However, challenges like hallucinations and bias must be addressed for safe integration into intensive care units (ICUs).

Keywords:
ChatGPTartificial intelligenceclinical decision supportcritical careintensive careintensive care unitlarge language model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Critical Care Medicine (CCM)
  • Large Language Models (LLMs)

Background:

  • Rapid advancements in artificial intelligence (AI) have led to the development of large language models (LLMs) with significant natural language processing capabilities.
  • The application of LLMs in health and medicine, particularly in critical care medicine (CCM), is gaining research interest.
  • Uncertainty remains regarding LLMs' ability to function as expert decision-support tools in intensive care units (ICUs).

Purpose of the Study:

  • To conduct a scoping review of LLM applications in CCM.
  • To identify the advantages, challenges, and future potential of LLMs in critical care.
  • To provide a comprehensive overview of the current landscape of LLMs in CCM.

Main Methods:

  • Adherence to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
  • Comprehensive literature search across seven major databases (PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, ACM Digital Library).
  • Inclusion of studies from the earliest available publication up to August 22, 2025.

Main Results:

  • 41 papers were selected from an initial 2342 retrieved.
  • LLMs are utilized in CCM for clinical decision support, medical documentation/reporting, and medical education/patient communication.
  • LLMs offer advantages over traditional AI in handling unstructured data but face challenges like hallucinations, bias, and privacy concerns.

Conclusions:

  • LLMs have the potential to become valuable tools in CCM, improving patient outcomes and healthcare delivery.
  • Future research should focus on enhancing LLM reliability, interpretability, scalability, and ethical guidelines.
  • Addressing challenges is crucial for realizing the full impact of LLMs in critical care.