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Related Experiment Videos

Real-World Performance of Artificial Intelligence in Diabetic Retinopathy Screening: A Systematic Review.

Saira Ahmed1

  • 1Medicine and Surgery, Frimley Health Foundation Trust, London, GBR.

Cureus
|June 16, 2026
PubMed
Summary

Artificial intelligence (AI) shows high accuracy in detecting diabetic retinopathy (DR), a leading cause of blindness. AI systems are feasible for screening programs, with potential for wider access in underserved areas.

Related Concept Videos

Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a major cause of preventable blindness globally.
  • Early detection and intervention through screening are crucial for managing DR.
  • Artificial intelligence (AI) offers a promising avenue for automated DR screening.

Purpose of the Study:

  • To systematically review the diagnostic performance of AI systems for DR detection.
  • To evaluate the real-world applicability of AI-based DR screening in various clinical settings.
  • To assess the potential of AI in improving screening accessibility, especially in resource-limited areas.

Main Methods:

  • Systematic literature search of PubMed, Embase, and Cochrane Library, supplemented by Google Scholar.
Keywords:
artificial intelligencedeep learningdiabetic retinopathyophthalmologyretinal screening

Related Experiment Videos

  • Study selection adhered to PRISMA 2020 guidelines.
  • Inclusion criteria focused on AI systems for DR detection using fundus imaging with reported diagnostic accuracy.

    • Main Results:

    • Thirty studies (2016-2025) were included, consistently showing high diagnostic performance for AI systems (sensitivity >85%, specificity >80%).
    • Large-scale and real-world studies confirmed AI's feasibility in national and community screening programs.
    • Smartphone-based and handheld systems show potential for expanding access in resource-limited settings.

    Conclusions:

    • AI demonstrates significant potential to enhance the efficiency and accessibility of diabetic retinopathy screening.
    • Further research is needed for external validation, standardization, and evaluation of long-term clinical outcomes before widespread adoption.
    • AI integration into healthcare systems requires further investigation for optimal implementation.