Evidence map›Paper›PMID 41272074›Full record

Observational studyScientific reports2025

Reach and implementation of human and AI-assisted diabetic retinopathy screening models in primary healthcare settings in India.

Anshul Chauhan, Luke Vale, Ankita Kankaria, Vishali Gupta, Mandeep Singh, Gagandeep Kaur, Sonam Kumar, Mitesh Yadav, Neha N, Basavaraj Tigari and 2 more

Abstract readObservational Study
In one paragraph

Observational study in Scientific reports, 2025. 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. Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Review
  2. Review
  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

12 authors.

Anshul ChauhanAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Luke ValeGlobal Health Economics Centre, London School of Hygiene and Tropical Medicine, Tavistock Place, London, UK.
Ankita KankariaDepartment of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Vishali GuptaAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Mandeep SinghAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Gagandeep KaurAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Sonam KumarAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Mitesh YadavAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Neha NAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Basavaraj TigariAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Sanjay BhadadaDepartment of Endocrinology, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Mona DuggalAdvanced Eye Centre (AEC), Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India. monaduggal2@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic retinopathy (DR) is a leading cause of preventable vision loss. While DR screening is critical, evidence on the reach and implementation of different screening models in primary healthcare settings is limited. This study evaluated the reach and implementation of DRS models in northern India using the RE-AIM framework. A pragmatic three-arm observational study was conducted between February 2023 and January 2024 in Block Boothgarh, a rural block in District Mohali, Punjab, comprising 30 villages with an estimated 120,000 residents. Household line listing was performed to identify individuals aged 30 years or older with diabetes. Participants (n = 600) were equally allocated to three screening models: facility-based screening at Health and Wellness Centres (HWC) by non-ophthalmologists, community-based AI-assisted screening at home, and standard care. Reach and implementation were assessed through quantitative data, field observations, and qualitative interviews with healthcare providers. Refusal for screening was higher in facility-based screening (40%, 135/340) and lower in community-based screening (13%, 31/240). Older individuals were more likely to decline participation, with a mean age of 62.0 years for males and 60.3 years for females. Reported barriers included existing medical conditions, mobility limitations, perceived good eye health, travel distance, and transportation difficulties. Concerns regarding long-term medication adherence also reduced uptake. Technical issues, including power outages, hardware or software malfunctions, suboptimal image quality, and lack of cooperation, further declined implementation. Adaptations, including the use of backup power generators, on-site troubleshooting, and provision of transport support, mitigated these barriers and improved overall implementation fidelity. Assessing reach is essential for the success of public health interventions. Using the RE-AIM framework, this study identified key barriers and adaptive strategies in DRS, enhancing both reach and implementation within primary healthcare settings. These findings can inform the integration of DRS models into comparable resource-constrained contexts, thereby improving overall effectiveness.Clinical Trial Registry of India (CTRI): 2022/10/046283.

Indexed as

Artificial IntelligenceDiabetic RetinopathyMass ScreeningPrimary Health CareAdultAgedFemaleHumansIndiaMaleMiddle AgedRural PopulationArtificial intelligenceDiabetic retinopathy screeningImplementationPragmatic trialReachRE-AIM

Identifiers

PMID41272074
PMCPMC12638931

What OpenQuestion holds

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LicenceCC BY
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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.