Large language models (LLMs) are emerging as a transformative tool in critical care medicine, a discipline that fundamentally relies on multidisciplinary collaboration, real-time monitoring, and timely intervention. With their capability to integrate and synthesize multi-modal data (e.g., clinical notes, physiological signals), LLMs demonstrate significant potential to enhance condition monitoring, provide clinical decision support, and improve overall workflow efficiency. However, the rigorous demands of critical care -including the needs for individualized clinical reasoning, accurate interpretation of dynamic high-stakes patient data, and strict adherence to evidence - based practice-present substantial challenges for the direct application of general - purpose LLMs. This review systematically explores the potential application pathways for LLMs in critical care medicine by integrating insights from technical explorations and clinical practice. Furthermore, we propose a future roadmap focusing on four key areas: deep integration into core clinical workflows, continuous technological refinement, establishment of robust standards and validation frameworks, and fostering effective collaboration between clinicians and engineers.