Evidence map›Paper›PMID 40490617›Full record

ReviewCanadian journal of anaesthesia = Journal canadien d'anesthesie2025

Large language models in perioperative medicine-applications and future prospects: a narrative review.

Arnaud Romeo Mbadjeu Hondjeu, Zi Ying Zhao, Luka Newton, Anass Ajenkar, Emily Hladkowicz, Karim Ladha, Duminda N Wijeysundera, Daniel I McIsaac

Abstract readReview
In one paragraph

Review in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Arnaud Romeo Mbadjeu HondjeuDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada. ambadjeu@toh.ca.ORCID 0000-0001-6470-4574
Zi Ying ZhaoDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada.
Luka NewtonDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada.
Anass AjenkarDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada.
Emily HladkowiczThe Ottawa Hospital, Ottawa, ON, Canada.
Karim LadhaDepartment of Anesthesiology & Pain Medicine, University of Toronto, Toronto, ON, Canada.
Duminda N WijeysunderaDepartment of Anesthesiology & Pain Medicine, University of Toronto, Toronto, ON, Canada.
Daniel I McIsaacDepartment of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada.

Funding

Non-US Government Research Support
6 · The paper itself

Abstract

purposeLarge 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

findingsWe 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.

conclusionsThe 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.

Indexed as

Artificial IntelligenceLanguageLinguisticsPerioperative CarePerioperative MedicineClinical Decision-MakingDelivery of Health CareHumansLarge Language Modelsartificial intelligenceChatGPTgenerative AIhealth carelarge language modelsmachine learning applicationsnatural language generationnatural language processingperioperative medicine

Identifiers

PMID40490617
PMCPMC12228656

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.