Evidence map›Paper›PMID 40509911›Full record

ArticleJournal of perioperative practice

Artificial intelligence in perioperative medicine education: A feasibility test of case-based learning.

Timothy Trewren, Nicholas Fitzgerald, Sarah Jaensch, Olivia Nguyen, Alexander Tsymbal, Christina Gao, Brandon Stretton, Stewart Anderson, D-Yin Lin, Dario Winterton and 4 more

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Article in Journal of perioperative practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
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

14 authors.

Timothy TrewrenDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.ORCID 0000-0003-4626-5007
Nicholas FitzgeraldDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Sarah JaenschDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Olivia NguyenDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Alexander TsymbalDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.ORCID 0009-0008-2841-2484
Christina GaoDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Brandon StrettonDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Stewart AndersonDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
D-Yin LinFlinders Medical Centre, Bedford Park, SA, Australia.
Dario WintertonHarvard Medical School, Boston, MA, USA.
Galina GheihmanHarvard Medical School, Boston, MA, USA.
Guy LudbrookDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.
Kelly BratkovicFlinders Medical Centre, Bedford Park, SA, Australia.
Stephen BacchiDepartment of Anaesthesia, Adelaide Medical School, The University of Adelaide, Adelaide, SA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of artificial intelligence in medicine is rapidly expanding. Large language models, such as ChatGPT, have the potential to enhance perioperative medicine through education and clinical practice. However, concerns remain regarding the accuracy of these models, particularly the risk of hallucinations, generating factually incorrect outputs. This feasibility test explores the use of a large language model-enabled platform to assist in case-based education in perioperative clinical cases.

methodsFive perioperative cases addressing core topics were developed and uploaded to a custom large language model platform. The large language model platform allows free-text questions to be asked to the artificial intelligence, which then uses the derived cases to provide answers. Anaesthetic trainees engaged with the artificial intelligence, asking questions to obtain information regarding history, examination, and investigations. Artificial intelligence question-and-answer pairs were then evaluated independently in duplicate for the presence of inappropriate responses, including hallucinations.

resultsThe large language model responded appropriately to nearly all questions, with no hallucinations observed. The proportion of questions that were answered appropriately was 99.3% (543/547). In the four instances of inappropriate responses, the large language model declined to provide information in the case description rather than hallucinate.

conclusionThe large language model appears capable of supporting the delivery of case-based perioperative medicine content with a high degree of accuracy.

Indexed as

Artificial IntelligencePerioperative MedicineProblem-Based LearningFeasibility StudiesHumansArtificial intelligenceCase-based learningLarge language modelsMachine learningMedical educationNatural language processing

Identifiers

PMID40509911
PMCPMC12712223

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.