Evidence map›Paper›PMID 37401955›Full record

ReviewMedizinische Klinik, Intensivmedizin und Notfallmedizin2023

[ChatGPT: curse or blessing in nursing care?]

Lars Krüger, Susanne Krotsetis, OpenAI’s Generative Pretrained Transformer 3 (GPT-3) Model, Peter Nydahl

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Medizinische Klinik, Intensivmedizin und Notfallmedizin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 32% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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

4 authors at 2 institutions in 1 country.

Lars KrügerHerz- und Diabeteszentrum NRW, Universitätsklinikum der Ruhr-Universität Bochum, Bad Oeynhausen, Deutschland.ORCID http://orcid.org/0000-0003-4234-0022
Susanne KrotsetisPflegeentwicklung und Pflegewissenschaft angegliedert der Pflegedirektion, des Universitätsklinikums Schleswig-Holstein, Campus Lübeck, Lübeck, Deutschland.
OpenAI’s Generative Pretrained Transformer 3 (GPT-3) Model
Peter NydahlPflegeforschung und -entwicklung, Pflegedirektion, Universitätsklinikum Schleswig-Holstein, Haus V40, Arnold-Heller-Str. 3, 24105, Kiel, Deutschland. peter.nydahl@uksh.de.ORCID http://orcid.org/0000-0002-5178-0364
University Hospital Schleswig-Holstein · DEUniversity Hospitals of the Ruhr-University of Bochum · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) has been used in healthcare for some years for risk detection, diagnostics, documentation, education and training and other purposes. A new open AI application is ChatGPT, which is accessible to everyone. The application of ChatGPT as AI in education, training or studies is currently being discussed from many perspectives. It is questionable whether ChatGPT can and should also support nursing professions in health care. The aim of this review article is to show and critically discuss possible areas of application of ChatGPT in theory and practice with a focus on nursing practice, pedagogy, nursing research and nursing development.

Indexed as

Artificial IntelligenceNursing CareEducation, NursingGenerative Artificial IntelligenceGermanyHumansNursing ResearchArtificial intelligenceChatbotEthicEvidence-based practiceNursingTeachingTechnical Support

Identifiers

PMID37401955
OpenAlexW4383058425

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.