Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model

The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams (‘screening’) on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care auto...

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Bibliographic Details
Published in:NPJ digital medicine 2023-03, Vol.6 (1), p.53-53, Article 53
Main Authors: Channa, Roomasa, Wolf, Risa M., Abràmoff, Michael D., Lehmann, Harold P.
Format: Article
Language:English
Subjects:
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Artificial intelligence
Biomedicine
Biotechnology
Blindness
Diabetes
Diabetic retinopathy
Digital technology
Disease prevention
Medical screening
Medical technology
Medicine
Medicine & Public Health
Perspective
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Summary:The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams (‘screening’) on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-023-00785-z