Perioperative medicine & outcomes
Large language models in perioperative medicine—applications and future prospects: a narrative review
Authors' conclusion
The integration of LLMs into perioperative medicine presents a significant opportunity to enhance patient care, clinical decision-making, and operational efficiency.
Study context
- Population
- Perioperative care (from surgical diagnosis to discharge postoperatively)
- Outcome
- Clinical applications of LLMs in perioperative medicine
Methodological detailsSearched MEDLINE, Google Scholar, and PubMed® for articles referencing large language models (LLMs) in perioperative care
- Searched MEDLINE, Google Scholar, and PubMed® for articles referencing LLMs in perioperative care
Main finding
As reported in the abstract
- LLMs may improve efficiency and accuracy of health care delivery by extracting and summarizing clinical data, making recommendations and addressing patient queries
- LLMs can be used for clinical decision-making support, surveillance tools, predictive modelling, and enhancement of medical research and education
Further findings
Challenges to clinical implementation
- Managing hallucinations, ensuring data security, and mitigating inherent biases
Original publication
Large language models in perioperative medicine—applications and future prospects: a narrative review. Can J Anaesth. Published 9 June 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →