Large Language Models in Emergency and Critical Care Medicine: A Comprehensive Review of Applications, Challenges, and Future Directions

Abstract

Abstract Emergency and critical care medicine (ECCM) requires the rapid synthesis of heterogeneous clinical data under extreme time constraints. Early artificial intelligence (AI) tools lacked the flexibility to manage real-word patient heterogeneity. Large language models (LLMs) offer a paradigm shift by demonstrating advanced natural language understanding, cross-task generalization, and context-sensitive reasoning, thereby bridging the gap between fragmented algorithms and holistic clinical decision support. The effective deployment of these models is grounded in four methodological pillars: domain adaptation, knowledge integration, multimodal and temporal modeling and transparency. Domain adaptation and knowledge integration specifically empower the context-sensitive reasoning required for high-stakes intensive care. This theoretical framework enables their application across clinical decision support, documentation optimization, medical education, and clinical research. Integrating continuous physiological waveforms with multi-omics data facilitates dynamic risk stratification for complex conditions like sepsis, while natural language-to-structured query language capabilities accelerate clinical data extraction and quality improvement. The transition of LLMs from experimental settings to routine clinical deployment remains constrained by model hallucinations, multimodal integration barriers, and unresolved ethical governance. Sustainable implementation requires a human-in-the-loop copilot design, rigorous multicenter prospective validation, and transparent regulatory frameworks. Addressing these challenges is essential to ensure technological innovations safely translate into measurable improvements in patient survival and clinical outcomes.

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