Evidence map›Paper›PMID 40248774›Full record

ArticleJournal of anaesthesiology, clinical pharmacology

Artificial intelligence enhanced Chatbot boom: A single center observational study to evaluate assistance in clinical anesthesiology.

Sowmya M Jois, Srinivasan Rangalakshmi, Sowmya Madihalli Janardhan Iyengar, Chethana Mahesh, Lairenjam Deepa Devi, Arun Kumar Namachivayam

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Article in Journal of anaesthesiology, clinical pharmacology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

  1. Observational
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sowmya M JoisDepartment of Anaesthesiology, RajaRajeswari Medical College, Bangalore, Affiliated to MGR University, Bengaluru, Karnataka, India.
Srinivasan RangalakshmiDepartment of Anaesthesiology, RajaRajeswari Medical College, Bangalore, Affiliated to MGR University, Bengaluru, Karnataka, India.
Sowmya Madihalli Janardhan IyengarDepartment of Anaesthesiology, RajaRajeswari Medical College, Bangalore, Affiliated to MGR University, Bengaluru, Karnataka, India.
Chethana MaheshDepartment of Anaesthesiology, RajaRajeswari Medical College, Bangalore, Affiliated to MGR University, Bengaluru, Karnataka, India.
Lairenjam Deepa DeviDepartment of Anaesthesiology, RajaRajeswari Medical College, Bangalore, Affiliated to MGR University, Bengaluru, Karnataka, India.
Arun Kumar NamachivayamDepartment of Biostatistics, Bapuji Dental College and Hospital, Davangere, Karnataka, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: The field of anaesthesiology and perioperative medicine has explored advancements in science and technology, ensuring precision and personalized anesthesia plans. The surge in the usage of chat-generative pretrained transformer (Chat GPT) in medicine has evoked interest among anesthesiologists to assess its performance in the operating room. However, there is concern about accuracy, patient privacy and ethics. Our objective in this study assess whether Chat GPT can provide assistance in clinical decisions and compare them with those of resident anesthesiologists. Material and Methods: In this cross-sectional study conducted at a teaching hospital, a set of 30 hypothetical clinical scenarios in the operating room were presented to resident anesthesiologists and Chat-GPT 4. The first five scenarios out of 30 were typed with three additional prompts in the same chat to determine if there was any detailing of answers. The responses were labeled and assessed by three reviewers not involved in the study. Results: The interclass coefficient (ICC) values show variation in the level of agreement between Chat GPT and anesthesiologists. For instance, the ICC of 0.41 between A1 and Chat GPT indicates a moderate level of agreement, whereas the ICC of 0.06 between A2 and Chat GPT suggests a comparatively weaker level of agreement. Conclusions: In this study, it was found that there were variations in the level of agreement between Chat GPT and resident anesthesiologists' response in terms of accuracy and comprehensiveness of responses in solving intraoperative scenarios. The use of prompts improved the agreement of Chat GPT with anesthesiologists.

Indexed as

Anesthesiaartificial intelligencedecision support systemsdeep learningethicsmachine learningperioperative care

Identifiers

PMID40248774
PMCPMC12002681

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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.