Efficacy of Using Large Language Model to Assist in Diabetic Retinopathy Detection
Abstract
With the increase in population and the rising prevalence of various diseases, the workload of disease diagnosis has sharply increased. The accessibility of healthcare services and long waiting times have become common issues in the public health medical system, with many primary patients having to wait for extended periods to receive medical services. There is an urgent need for rapid, accurate, and low-cost diagnostic services.
Conditions: Diagnosis, Diabetic Retinopathy.
Primary outcome: AUROC of the self-evaluation tool.
Enrollment: 535 participants.
How strong is the evidence?
Evidence Hub reads this as a clinical trial, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 535 participants or records.
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