Back to all news
UK Researchers Launch Unbiased AI Testing Platform for Diabetic Retinopathy Screening
Researchers unveiled a new independent platform for fair, large-scale evaluation of commercial AI algorithms detecting diabetic eye disease within the NHS.
Key Details
- 1First real-world head-to-head platform to evaluate commercial AI algorithms for NHS use in detecting diabetic eye disease.
- 2Eight CE marked AI algorithms tested on 1.2 million retinal images from one of the UK’s largest and most diverse screening programs.
- 3Algorithm performance (accuracy) ranged from 83.7% to 98.7% for clinically relevant diabetic eye disease, outperforming or matching human graders.
- 4Algorithms’ accuracy remained consistent across varied ethnic groups; first such assessment at this scale.
- 5AI analyzed images in 240ms–45s compared to up to 20 minutes for humans, vastly improving efficiency.
- 6Funded by NHS Transformation Directorate, The Health Foundation, and Wellcome Trust; study published in The Lancet Digital Health.
Why It Matters
This platform addresses critical gaps in algorithmic fairness, transparency, and efficiency for imaging AI, setting a rigorous new standard for evaluation and deployment in national health systems. Its scalable, unbiased testing approach could serve as a blueprint for clinical AI adoption across radiology and other disease areas.
Source
EurekAlert
Related News
•EurekAlert
AI-Driven CT Imaging Predicts Cardiac Events in Large UK Cohort
An AI tool analyzing CCTA images can predict future cardiovascular events and death in patients with suspected stable coronary artery disease.
•EurekAlert
AI Tool from UCLA Targets Undiagnosed Alzheimer's and Diagnostic Disparity
UCLA researchers developed an AI model using EHR data to better detect undiagnosed Alzheimer's disease, especially in underrepresented groups.
•EurekAlert
AI Multimodal Models Improve Breast Cancer Recurrence Risk Prediction
Initial results from an ECOG-ACRIN and Caris Life Sciences collaboration show AI-driven multimodal models can more accurately predict recurrence risk in early-stage breast cancer.