  With the rapid development of artificial intelligence (AI), large language
models (LLMs) have shown strong capabilities in natural language understanding,
reasoning, and generation, attracting amounts of research interest in applying
LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis
and treatment for critically ill patients who often require intensive
monitoring and interventions in intensive care units (ICUs). Can LLMs be
applied to CCM? Are LLMs just like stochastic parrots or ICU experts in
assisting clinical decision-making? This scoping review aims to provide a
panoramic portrait of the application of LLMs in CCM. Literature in seven
databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE
Xplore, and ACM Digital Library, were searched from January 1, 2019, to June
10, 2024. Peer-reviewed journal and conference articles that discussed the
application of LLMs in critical care settings were included. From an initial
619 articles, 24 were selected for final review. This review grouped
applications of LLMs in CCM into three categories: clinical decision support,
medical documentation and reporting, and medical education and doctor-patient
communication. LLMs have advantages in handling unstructured data and do not
require manual feature engineering. Meanwhile, applying LLMs to CCM faces
challenges, including hallucinations, poor interpretability, bias and alignment
challenges, and privacy and ethics issues. Future research should enhance model
reliability and interpretability, integrate up-to-date medical knowledge, and
strengthen privacy and ethical guidelines. As LLMs evolve, they could become
key tools in CCM to help improve patient outcomes and optimize healthcare
delivery. This study is the first review of LLMs in CCM, aiding researchers,
clinicians, and policymakers to understand the current status and future
potentials of LLMs in CCM.
