What question did this study set out to answer?

To evaluate the diagnostic performance, feasibility, and cost-effectiveness of an AI-aided screening model for diabetic retinopathy in rural settings.

May 10, 2026NATIONAL BOARD OF EXAMINATIONS JOURNAL OF MEDICAL SCIENCESOpen Access

Diabetic Retinopathy Screening with Artificial Intelligence in Remote Areas

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To evaluate the diagnostic performance, feasibility, and cost-effectiveness of an AI-aided screening model for diabetic retinopathy in rural settings.
Prospective cross-sectional accuracy trial conducted in rural primary health centres and outreach clinics.
Non-mydriatic fundus photography was performed by trained technicians on diabetic participants.
A deep learning AI algorithm analyzed retinal photos to identify vision-threatening diabetic retinopathy.
Sensitivity and specificity of the AI system for detecting VTDR were 89.4% and 87.2%, respectively.
Positive predictive value was 61.8% and negative predictive value was 96.3%.
AI-assisted screening increased the number of people screened by 35% and reduced the cost per person screened by 40%.

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Implication

Randomized trial evaluates AI's cost-effectiveness and diagnostic performance in rural diabetic retinopathy screening, indicating its potential to reduce blindness.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6a001ff2c8f74e3340f9b235 https://doi.org/10.61770/nbejms.2026.v04.i05.003

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Implication

Randomized trial evaluates AI's cost-effectiveness and diagnostic performance in rural diabetic retinopathy screening, indicating its potential to reduce blindness.

Diabetic Retinopathy Screening with Artificial Intelligence in Remote Areas

Key Points

Discussion

Implication

Cite This Study

Diabetic Retinopathy Screening with Artificial Intelligence in Remote Areas

Key Points

Discussion

Implication

Cite This Study

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