Evidence map›Paper›PMID 36973403›Full record

ReviewNPJ digital medicine2023

Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model.

Roomasa Channa, Risa M Wolf, Michael D Abràmoff, Harold P Lehmann

Open access · goldFull text readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
5.0field-weighted citation impact, top 4% 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

17 citing papers in PubMed, 22 citations in OpenAlex.

  1. Trial
  2. Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  3. Article
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  5. A Review of Recent Developments in Artificial Intelligence and Big Data Technologies for Ophthalmology Referrals and Clinical Practice.Medical science monitor : international medical journal of experimental and clinical research · 2026
    Review
  6. Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025
    Review
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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 3 institutions in 1 country.

Roomasa ChannaDepartment of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA. rchanna@wisc.edu.
Risa M WolfDepartment of Pediatrics, Division of Endocrinology, Johns Hopkins Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-7674-520X
Michael D AbràmoffDepartment of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA.ORCID http://orcid.org/0000-0002-3490-0037
Harold P LehmannDepartment of Medicine, Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-7698-219X
Johns Hopkins University · USUniversity of Iowa · USUniversity of Wisconsin–Madison · US

Funding

Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19R01EY033233 · NEI · JOHNS HOPKINS UNIVERSITY · PI WOLF, RISA MICHELLE · 2021 to 2023
$1.9M
NEI NIH HHS R01 EY033233
6 · The paper itself

Abstract

The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams ('screening') on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.

Identifiers

PMID36973403
PMCPMC10042864
OpenAlexW4361007013

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read19
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.