Evidence map›Paper›PMID 36202424›Full record

ArticleBMJ health & care informatics2022

Three-year trends in literature on artificial intelligence in ophthalmology and vision sciences: a protocol for bibliometric analysis.

Hayley Monson, Jeff Demaine, Laura Banfield, Tina Felfeli

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

4 authors.

Hayley MonsonDepartment of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.
Jeff DemaineLibrary Department, McMaster University, Hamilton, Ontario, Canada.
Laura BanfieldLibrary Department, McMaster University, Hamilton, Ontario, Canada.
Tina FelfeliDepartment of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada tina.felfeli@mail.utoronto.ca.ORCID http://orcid.org/0000-0002-0927-3086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe aim of this study is to provide an insight into the literature at the intersection of artificial intelligence and ophthalmology. METHODS AND ANALYSIS: The project will be performed in four key stages: formulation of search terms, literature collection, literature screening and literature analysis. A comprehensive search of databases including Scopus, Web of Science, Dimensions and Cochrane will be conducted. The Distiller SR software will be used for manual screening all relevant articles. The selected articles will be analysed via R Bibliometrix, a program for mathematical analysis of large sets of literature, and VOSviewer, which creates visual representations of connections between articles. ETHICS AND DISSEMINATION: This study did not require research ethics approval given the use of publicly available data and lack of human subjects. The results will be presented at scientific meetings and published in peer-reviewed journals.

Indexed as

OphthalmologyArtificial IntelligenceBibliometricsDatabases, FactualHumansdata sciencedeep learninginformaticsmachine learningmedical informatics

Identifiers

PMID36202424
PMCPMC9540841

What OpenQuestion 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 OpenQuestion graph.