Abstract
PURPOSE: Large language models (LLMs) are a subset of artificial intelligence (AI) and linguistics designed to help computers understand and analyze human language. Clinical applications of LLMs have recently been recognised for their potential enhanced analytic capacity. Availability and performance of LLMs are expected to increase substantially over time with a significant impact on patient care and health care provider workflow. Despite increasing recognition of LLMs, insights on the utilities, associated benefits and limitations are scarce among perioperative clinicians. In this narrative review, we delve into the functionalities and prospects of existing LLMs and their clinical application in perioperative medicine. Furthermore, we summarize challenges and constraints that must be addressed to fully realize the potential of LLMs. SOURCE: We searched MEDLINE, Google Scholar, and PubMed® databases for articles referencing LLMs in perioperative care. PRINCIPAL FINDINGS: We found that in the perioperative setting (from surgical diagnosis to discharge postoperatively), LLMs have the potential to improve the efficiency and accuracy of health care delivery by extracting and summarizing clinical data, making recommendations on the basis of these findings, as well as addressing patient queries. Moreover, LLMs can be used for clinical decision-making support, surveillance tools, predictive modelling, and enhancement of medical research and education. CONCLUSIONS: The integration of LLMs into perioperative medicine presents a significant opportunity to enhance patient care, clinical decision-making, and operational efficiency. These models can streamline processes, provide personalized patient education, and offer robust decision support. Nevertheless, their clinical implementation requires addressing several key challenges, including managing hallucinations, ensuring data security, and mitigating inherent biases. If these challenges are met, LLMs can revolutionize perioperative practice, improving both patient outcomes and clinician workflow.