Large language models in critical care

Laurens A Biesheuvel ¹, Jessica D Workum ^2,3, Merijn Reuland ¹

⁰Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam Institute for Immunity and Infectious Diseases, Amsterdam Cardiovascular Science, Amsterdam UMC, Vrije Universiteit, University of Amsterdam, Amsterdam, The Netherlands.

Journal of Intensive Medicine +

|

April 17, 2025

View abstract on PubMed

Summary

Large language models (LLMs) offer transformative potential in critical care medicine by enhancing data analysis and clinical support. Responsible implementation and clinician training are vital to harness these AI advancements safely and effectively.

Area Of Science

Artificial Intelligence in Medicine
Natural Language Processing Applications
Critical Care Informatics

Background

Large language models (LLMs) and chat generative pre-trained transformers (ChatGPT) represent a significant advancement in natural language processing (NLP).
Critical care medicine generates vast amounts of unstructured data, presenting unique challenges and opportunities for AI.
The capabilities of LLMs in understanding and generating human-like text are highly relevant to clinical environments.

Purpose Of The Study

To explore the potential applications of LLMs in critical care medicine.
To identify the benefits and challenges associated with integrating LLMs into critical care settings.
To discuss the role of Hybrid AI and responsible implementation strategies.

Main Methods

Review of current LLM capabilities in NLP.
Analysis of potential use cases in critical care: administrative support, clinical decision support, patient communication, and data quality improvement.
Discussion of challenges including AI hallucinations, ethical concerns, and the need for AI literacy.
Exploration of Hybrid AI approaches combining LLMs with traditional machine learning.

Main Results

LLMs show promise for automating documentation, summarizing patient charts, aiding diagnostics, and personalizing communication.
Key challenges include the risk of inaccurate information ('hallucinations'), ethical dilemmas, and the necessity for clinician AI literacy.
Hybrid AI models can leverage LLM strengths while mitigating weaknesses.

Conclusions

LLMs have the potential to significantly transform critical care practices.
Cautious integration, regulatory compliance, and ongoing validation are essential for safe and effective use.
Prioritizing responsible deployment and comprehensive clinician training is crucial to maximize benefits and ensure patient safety.

Keywords:

Artificial intelligence Critical care medicine Intensive care medicine Large language models Machine learning Natural language processing

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