Primer on large language models: an educational overview for intensivists.

Area of Science:

• Artificial Intelligence in Medicine
• Machine Learning Applications
• Clinical Informatics

Background:

• Artificial intelligence (AI) and machine learning (ML) are rapidly advancing in healthcare.
• Large Language Models (LLMs), a subset of ML, process text to understand language, semantics, and context.
• Intensivists require foundational knowledge of LLMs to navigate emerging medical literature and applications.

Purpose of the Study:

• To provide intensivists with foundational knowledge of LLMs.
• To guide critical care professionals in evaluating and approaching LLM literature.
• To highlight the potential and challenges of LLMs in critical care settings.

Main Methods:

• This is an educational primer, not a research study.
• It synthesizes current understanding of LLM capabilities and limitations.
• Focuses on conceptual understanding and literature appraisal for clinical application.

Main Results:

• LLMs show potential in critical care for triage, documentation, diagnostics, and prognostics.
• LLMs can effectively answer critical care-related clinical questions.
• Applications extend to post-ICU rehabilitation and patient/family education.

Conclusions:

• LLMs present significant opportunities for enhancing critical care patient management and support.
• Challenges including bias, reliability, and transparency must be addressed for safe integration.
• Rigorous validation and ethical considerations are crucial for responsible LLM deployment in AI-driven healthcare.