Evidence mapPaperPMID 41737862Full record

ArticleClinical ophthalmology (Auckland, N.Z.)2026

Patient Perspectives on Artificial Intelligence-Based Diabetic Retinopathy Screening at an Urban US Medical Center.

Zainab Rustam, Yangyiran Xie, Jose Amezcua Moreno, Diep Tran, Gina Zhu, Sophia E Yu, Aregnazan Sandrosyan, Cindy X Cai

Abstract read
In one paragraph

Article in Clinical ophthalmology (Auckland, N.Z.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zainab Rustam *Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID 0000-0001-8686-3517
Yangyiran Xie *Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
Jose Amezcua MorenoWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Diep TranWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Gina ZhuWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Sophia E YuWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.ORCID 0009-0000-9408-2972
Aregnazan SandrosyanPaul Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA.ORCID 0009-0001-3645-8924
Cindy X CaiWilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Funding

Impact of Social Determinants of Health in Diabetic RetinopathyK23EY033440 · JOHNS HOPKINS UNIVERSITY · 2025 to 2025
$189k
NEI NIH HHS K23 EY033440
6 · The paper itself

Abstract

Introduction: Fully autonomous artificial intelligence (AI) can improve access to diabetic retinopathy (DR) screening. In this study, we explore patient perspectives on the use of AI in DR screening. Methods: Adults with diabetes at an urban academic medical center were recruited to undergo imaging with a handheld AI fundus camera and respond to a survey on awareness of AI in healthcare, trust in AI systems, perceived efficiency of AI, preferences for personal interaction, and overall receptivity of AI in DR screening. Responses were stratified by sociodemographic and neighborhood characteristics. Results: A total of 100 participants were included (mean age 60 years; 52% female; 24% Hispanic, 20% non-Hispanic Black, 31% non-Hispanic White, 25% other). Most were aware of AI (78%) and its use in healthcare (70%), but fewer of its application to eye disease (46%). Most believed AI could improve accuracy and protect confidentiality (both 77%), yet 83% preferred physician oversight and would trust AI more if it were supervised by a doctor. Overall, 76% were comfortable with AI as part of the eye exam and 92% were satisfied with AI-based screening. Despite this, only 31% felt AI could replace a doctor visit and the majority of participants (94%) believed doctors will always remain responsible for diagnosing, even if AI was evaluating the scans. Conclusion: We found that while participants were generally comfortable with the use of AI as part of the eye examination, most would trust AI more if it were supervised by a doctor and did not believe that AI-based DR screening could replace a doctor's visit. Implementation of autonomous AI-based DR screening should address the mismatch between the intended use of fully autonomous AI screening and participants' understanding of the role that would mean for physicians.

Indexed as

artificial intelligencediabetic retinopathyfundus photographyhealth disparities

Identifiers

PMID41737862
PMCPMC12927760

What Socratic 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 Socratic graph.