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Artificial Intelligence Algorithms in Diabetic Retinopathy Screening

  • Microvascular Complications: Retinopathy (R Channa, Section Editor)
  • Published:
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Abstract

Purpose of Review

In this review, we focus on artificial intelligence (AI) algorithms for diabetic retinopathy (DR) screening and risk stratification and factors to consider when implementing AI algorithms in the clinic.

Recent Findings

AI algorithms have been adopted, and have received regulatory approval, for automated detection of referable DR with clinically acceptable diagnostic performance. While these metrics are an important first step, performance metrics that go beyond measures of technical accuracy are needed to fully evaluate the impact of AI algorithm on patient outcomes.

Summary

Recent advances in AI present an exciting opportunity to improve patient care. Using DR as an example, we have reviewed factors to consider in the implementation of AI algorithms in real-world clinical practice. These include real-world evaluation of safety, efficacy, and equity (bias); impact on patient outcomes; ethical, logistical, and regulatory factors.

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Authors and Affiliations

  1. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA

    Sidra Zafar & Heba Mahjoub

  2. Department of Ophthalmology, New York University School of Medicine, New York, NY, USA

    Nitish Mehta

  3. Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA

    Amitha Domalpally & Roomasa Channa

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  1. Sidra Zafar
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  2. Heba Mahjoub
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Correspondence to Roomasa Channa.

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This article is part of the Topical Collection on Microvascular Complications: Retinopathy

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Zafar, S., Mahjoub, H., Mehta, N. et al. Artificial Intelligence Algorithms in Diabetic Retinopathy Screening. Curr Diab Rep 22, 267–274 (2022). https://doi.org/10.1007/s11892-022-01467-y

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