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Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. British Journal of Ophthalmology 2021, 105(5):723-728. doi:10.1136/bjophthalmol-2020-316594. Funded in part by the NIHR. The authors declare no competing interests.","title":"Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC8077216/","date":"2020-06-30","written_by":"independent","quote":"The algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders."},{"kind":"assessment report","citation":"UK National Screening Committee (UK NSC) (2026). UK NSC consults on use of artificial intelligence in the diabetic eye screening programme (UK NSC blog, on its 2025 evidence summary), Paragraph on the 2024 resubmission; 'The evidence' section.","title":"UK NSC consults on use of artificial intelligence in the diabetic eye screening programme (UK NSC blog, on its 2025 evidence summary)","url":"https://nationalscreening.blog.gov.uk/2026/05/11/uk-nsc-consults-on-use-of-artificial-intelligence-in-the-diabetic-eye-screening-programme/","date":"2026-05-11","written_by":"independent","quote":"The new review concludes that some ARIAS are as good as the first human grader and could be tested in real world NHS services, enabling information to be collected about the effect of ARIAS on patients, staff, and costs."}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].upstream_sources":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"strategic_assessments[]":{"state":"unresolved","review_state":"not_assessed","coverage":"partial","family":"strategic assessments"},"summary":{"state":"value_present","value":{"kind":"record_fields","fields":{"summary":"In 30,405 English screening episodes, EyeArt flagged 95.7% of referable retinopathy and all moderate-to-severe and proliferative disease. 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The episodes came from the North East London, South East London and Gloucestershire screening programs. EyeArt graded the images on servers at Homerton Hospital from January to April 2017, in parallel with usual care. The final human grades served as the reference. EyeArt flagged 95.7% of episodes with referable retinopathy and all 462 with moderate-to-severe non-proliferative or proliferative disease. It agreed with a human grade of no retinopathy 68% of the time. About half of the episodes, 15,091 of 30,405, would still have needed human grading. The authors conclude that EyeArt could halve the workload of human graders. Since the pathway did not change, no work was saved during the study. 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