Evidence mapPaperPMID 40515637Full record

ArticleClinical & experimental ophthalmology

Barriers and Enablers Influencing the Implementation of Artificial Intelligence for Diabetic Retinopathy Screening in Clinical Practice: A Scoping Review.

Jenny Tran, Jose J Estevez, Natasha J Howard, Saravana Kumar

Abstract readScoping Review
In one paragraph

Article in Clinical & experimental ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  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

4 authors.

Jenny TranCollege of Medicine and Public Health, Flinders University, Adelaide, Australia.ORCID 0009-0009-9329-1567
Jose J EstevezDepartment of Ophthalmology, Flinders Centre for Ophthalmology, Eye and Vision Research, Flinders University, Adelaide, Australia.ORCID 0000-0003-2722-0700
Natasha J HowardSouth Australian Health and Medical Research Institute, Adelaide, Australia.ORCID 0000-0002-8099-3107
Saravana KumarUniSA Allied Health and Human Performance, University of South Australia, Adelaide, Australia.ORCID 0000-0002-4003-4411

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic retinopathy is a leading cause of preventable blindness worldwide. Meanwhile, artificial intelligence is rapidly growing in clinical utility within medicine. This scoping review aims to identify and summarise existing literature on the barriers and enablers of clinical applications of artificial intelligence systems for the screening of diabetic retinopathy.

methodsUtilising a systematic approach and the PRISMA-ScR protocol for conducting scoping reviews, searches were performed in MEDLINE, Embase, Emcare, Cochrane, CINAHL, ProQuest, Scopus and grey literature (Australian Indigenous Health InfoNet). Two reviewers independently reviewed the records. A third reviewer provided consensus. Data extraction and synthesis in narrative form ensued.

resultsA total of 3844 articles were screened, of which 18 were selected. Published between 2018 and 2023, the selected studies varied in study design and were conducted across 10 countries. Several barriers and enablers were identified and categorised into four domains: healthcare system, healthcare professional, healthcare user and information technology. Within the healthcare system, clinical efficiency was reported on most frequently. Concerning the healthcare professional, education was most frequently discussed. Within healthcare user, studies most frequently identified factors pertaining to patient outcomes, while diagnostic performance was most frequently explored under the information technology domain.

conclusionsAs evidence for the efficacy of artificial intelligence for diabetic retinopathy screening grows, barriers to and enablers for its uptake in clinical practice are paramount considerations. Translating the knowledge of systems, provider, consumer and technological factors informs clinical strategies, ultimately facilitating the sustainable and effective implementation of this novel technology for screening practices.

Indexed as

Artificial IntelligenceDiabetic RetinopathyMass ScreeningHumansartificial intelligencebarriersdiabetic retinopathyenablersimplementation science

Identifiers

PMID40515637
PMCPMC12516656

What Socratic 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 Socratic graph.